Configuring a user equipment for machine learning

ABSTRACT

Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

FIELD OF TECHNOLOGY

The following relates to wireless communications, including configuringa user equipment (UE) for machine learning.

BACKGROUND

Wireless communications systems are widely deployed to provide varioustypes of communication content such as voice, video, packet data,messaging, broadcast, and so on. These systems may be capable ofsupporting communication with multiple users by sharing the availablesystem resources (e.g., time, frequency, and power). Examples of suchmultiple-access systems include fourth generation (4G) systems such asLong Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, orLTE-A Pro systems, and fifth generation (5G) systems which may bereferred to as New Radio (NR) systems. These systems may employtechnologies such as code division multiple access (CDMA), time divisionmultiple access (TDMA), frequency division multiple access (FDMA),orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonalfrequency division multiplexing (DFT-S-OFDM). A wireless multiple-accesscommunications system may include one or more base stations or one ormore network access nodes, each simultaneously supporting communicationfor multiple communication devices, which may be otherwise known as userequipment (UE).

In some examples, a wireless communications system may support machinelearning. Machine learning may be described as a branch of artificialintelligence that provides systems the ability to improve and learn fromexperience. In some examples, a network may configure a UE for machinelearning and the UE may utilize machine learning to perform tasks suchas cell reselection, beam failure, beam management, etc.

SUMMARY

The described techniques relate to improved methods, systems, devices,and apparatuses that support configuring a user equipment (UE) formachine learning. For example, the described techniques provide for a UEto obtain a neural network function, a machine learning model, and acorresponding set of parameters from a network.

In some examples, the UE may transmit capability information to thenetwork. The capability information may include one or more of a list ofpotential neural network functions, a list of potential machine learningmodels, or an indication of whether or not the UE may request machinelearning. Based on the capability information, the network may select aset of neural network functions, a set of machine learning models, andsets of corresponding parameters and in some examples, may indicate themto the UE.

In some examples, the UE may send a message to the network requesting toimplement machine learning (e.g., based on some trigger). The messagemay include an indication of a neural network function, a neural networkmodel, and a corresponding parameter set. In response to the requestmessage, the network may configure the machine learning model and thecorresponding parameters at the UE. When the UE obtains the machinelearning model and the corresponding parameter set, the network mayactivate machine learning at the UE and the UE utilize machine learningto perform one or more tasks. The techniques as described herein maysupport machine learning at a UE. Machine learning may allow the UE toperform tasks with little or no instruction from the network. As such,machine learning at the UE, as supported by the techniques describedherein, may result in less signaling overhead and reduce powerconsumption at the UE.

A method for wireless communication at a UE is described. The method mayinclude receiving a machine learning model of one or more machinelearning models, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to a neural networkfunction of one or more neural network functions, where the one or moremachine learning models, the one or more neural network functions, orany combination thereof may be associated with a machine learning modelrepository (MR) that is included in or coupled with a base station andreceiving, from the base station, an activation message for the machinelearning model, the neural network function, or both.

An apparatus for wireless communication at a UE is described. Theapparatus may include a processor, memory coupled with the processor,and instructions stored in the memory. The instructions may beexecutable by the processor to cause the apparatus to receive a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith a base station and receive, from the base station, an activationmessage for the machine learning model, the neural network function, orboth.

Another apparatus for wireless communication at a UE is described. Theapparatus may include means for receiving a machine learning model ofone or more machine learning models, a set of parameters correspondingto the machine learning model, or a configuration corresponding to aneural network function of one or more neural network functions, wherethe one or more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with a base station and meansfor receiving, from the base station, an activation message for themachine learning model, the neural network function, or both.

A non-transitory computer-readable medium storing code for wirelesscommunication at a UE is described. The code may include instructionsexecutable by a processor to receive a machine learning model of one ormore machine learning models, a set of parameters corresponding to themachine learning model, or a configuration corresponding to a neuralnetwork function of one or more neural network functions, where the oneor more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with a base station andreceive, from the base station, an activation message for the machinelearning model, the neural network function, or both.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for transmitting, to thebase station, a request message that includes an indication of themachine learning model, the neural network function, or both, wherereceiving the machine learning model, the neural network function, orboth may be based on the request message.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from thebase station, signaling indicating a first set of machine learningmodels included in a blacklist, a second set of machine learning modelsincluded in a whitelist, or both, where transmitting the request messagemay be based on the machine learning model being included in thewhitelist, excluded from the blacklist, or both.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, each machine learning modelof the one or more machine learning models may be associated with arespective scope corresponding to a location, a network slice, a deepneural network (DNN), a public land mobile network (PLMN), a UE type, aradio resource control (RRC) state, a communication service, acommunication configuration, or any combination thereof and transmittingthe request message may be based on a trigger event that includes the UEhaving a condition that may be within the respective scope of themachine learning model.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the request message includesan indication of the trigger event.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, transmitting the requestmessage may include operations, features, means, or instructions fortransmitting a UE assistance information message that includes therequest message.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, transmitting the requestmessage may include operations, features, means, or instructions fortransmitting RRC signaling that includes the request message.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for transmitting therequest message includes transmitting the request message to a centralunit-control plane (CU-CP) entity included in the base station andreceiving the machine learning model, the set of parameters, or theconfiguration includes receiving the machine learning model, the set ofparameters, or the configuration from the CU-CP entity.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for determining an addressfor the machine learning model, the set of parameters, or theconfiguration based on an associated ID and an associated rule, wherereceiving the machine learning model, the set of parameters, or theconfiguration may be based on a download of the machine learning model,the set of parameters, or the configuration from the machine learning MRbased on the address.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for determining an addressfor a second machine learning model, a second set of parameterscorresponding to the second machine learning model, or a secondconfiguration corresponding to a second neural network function of theone or more neural network functions based on an associated ID and anassociated rule and initiating an upload of the second machine learningmodel, the second set of parameters, or the second configuration to themachine learning MR based on the address for the second machine learningmodel, the second set of parameters, or the second configuration.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving an addressfor the machine learning model, the set of parameters, or theconfiguration from a central unit-machine learning plane (CU-XP) entityincluded in the base station, where receiving the machine learningmodel, the set of parameters, or the configuration may be based on adownload of the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based on the address.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving an addressfor a second machine learning model, a second set of parameterscorresponding to the second machine learning model, or a secondconfiguration corresponding to a second neural network function of theone or more neural network functions from a CU-XP entity included in thebase station and initiating an upload of the second machine learningmodel, the second set of parameters, or the second configuration to themachine learning MR based on the address for the second machine learningmodel, the second set of parameters, or the second configuration.

A method for wireless communication at a base station is described. Themethod may include transmitting, to the UE, a machine learning model ofone or more machine learning models, a set of parameters correspondingto the machine learning model, or a configuration corresponding to aneural network function of one or more neural network functions, wherethe one or more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with the base station andtransmitting, to the UE, an activation message for the machine learningmodel, the neural network function, or both.

An apparatus for wireless communication at a base station is described.The apparatus may include a processor, memory coupled with theprocessor, and instructions stored in the memory. The instructions maybe executable by the processor to cause the apparatus to transmit, tothe UE, a machine learning model of one or more machine learning models,a set of parameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station and transmit, to the UE, an activation message forthe machine learning model, the neural network function, or both.

Another apparatus for wireless communication at a base station isdescribed. The apparatus may include means for transmitting, to the UE,a machine learning model of one or more machine learning models, a setof parameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station and means for transmitting, to the UE, anactivation message for the machine learning model, the neural networkfunction, or both.

A non-transitory computer-readable medium storing code for wirelesscommunication at a base station is described. The code may includeinstructions executable by a processor to transmit, to the UE, a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station and transmit, to the UE, an activation message forthe machine learning model, the neural network function, or both.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from the UE,a request message that includes an indication of the machine learningmodel, the neural network function, or both, where transmitting themachine learning model, the neural network function, or both may bebased on the request message.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for transmitting, to theUE, signaling indicating a first set of machine learning models includedin a blacklist, a second set of machine learning models included in awhitelist, or both, where the machine learning model may be included inthe whitelist, excluded from the blacklist, or both.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, each machine learning modelof the one or more machine learning models may be associated with arespective scope corresponding to a location, a network slice, a DNN, aPLMN, a UE type, an RRC state, a communication service, a communicationconfiguration, or any combination thereof and receiving the requestmessage may be based on a trigger event that includes the UE having acondition that may be within the respective scope of the machinelearning model.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, the request message includesan indication of the trigger event.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, receiving the request messagemay include operations, features, means, or instructions for receiving aUE assistance information message that includes the request message.

In some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein, receiving the request messagemay include operations, features, means, or instructions for receivingRRC signaling that includes the request message.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving the requestmessage at a CU-CP entity included in the base station, forwarding therequest message from the CU-CP entity to a CU-XP entity included in thebase station, and downloading, to the CU-CP entity, the machine learningmodel, the set of parameters, or the configuration from the machinelearning MR based on the request message, where transmitting the machinelearning model, the set of parameters, or the configuration to the UEmay be based on the downloading.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from the UE,an address for the machine learning model, the set of parameters, or theconfiguration and downloading, for the UE, the machine learning model,the set of parameters, or the configuration from the machine learning MRbased on the address.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from the UE,an address for a second machine learning model, a second set ofparameters corresponding to the second machine learning model, or asecond configuration corresponding to a second neural network functionof the one or more neural network functions and uploading the secondmachine learning model, the second set of parameters, or the secondconfiguration to the machine learning MR.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from the UEat a CU-XP entity included in the base station, an ID associated withthe machine learning model, the set of parameters, or the configuration,determining an address for the machine learning model, the set ofparameters, or the configuration based at least in part on the ID, anddownloading, for the UE, the machine learning model, the set ofparameters, or the configuration from the machine learning MR based onthe address, where transmitting the machine learning model, the set ofparameters, or the configuration to the UE may be based on thedownloading.

Some examples of the method, apparatuses, and non-transitorycomputer-readable medium described herein may further includeoperations, features, means, or instructions for receiving, from the UEat a CU-XP entity included in the base station, an ID associated with asecond machine learning model, a second set of parameters correspondingto the second machine learning model, or a second configurationcorresponding to a second neural network function of the one or moreneural network functions, determining an address for the second machinelearning model, the second set of parameters, or the secondconfiguration based at least in part on the ID, and uploading, to themachine learning MR, the second machine learning model, the second setof parameters, or the second configuration based on the address.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a wireless communications system thatsupports configuring a user equipment (UE) for machine learning inaccordance with aspects of the present disclosure.

FIG. 2A illustrates an example of a wireless communications system thatsupports configuring a UE for machine learning in accordance withaspects of the present disclosure.

FIG. 2B illustrates an example of a protocol stack that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIG. 3 through 6 illustrate examples of a process flow that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIGS. 7 and 8 show block diagrams of devices that support configuring aUE for machine learning in accordance with aspects of the presentdisclosure.

FIG. 9 shows a block diagram of a communications manager that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIG. 10 shows a diagram of a system including a device that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIGS. 11 and 12 show block diagrams of devices that support configuringa UE for machine learning in accordance with aspects of the presentdisclosure.

FIG. 13 shows a block diagram of a communications manager that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIG. 14 shows a diagram of a system including a device that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

FIGS. 15 through 18 show flowcharts illustrating methods that supportconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure.

DETAILED DESCRIPTION

A user equipment (UE) may utilize machine learning to perform differentcommunication procedures. For example, the UE may utilize machinelearning to perform cell reselection, channel state information (CSI)reporting, etc. To utilize machine learning, the UE may obtain knowledgeof a neural network function, a machine learning model, andcorresponding parameters. Improved solutions may be desired to supportthe configuration of machine learning at the UE (e.g., the provision ofneural network functions, machine learning models, and correspondingparameters to a UE).

Described herein are improved architectures and techniques by which anetwork may configure a UE to utilize machine learning. In someexamples, a base station may include multiple network entities such as,for example, a central unit user plane (CU-UP), a central unit controlplane (CU-CP), and a distributed unit (DU). In some cases, the basestation may additionally or alternatively include another central unitthat is configured to facilitate the exchange of messages pertaining tomachine learning (e.g., a central unit machine learning plane (CU-XP)).Further, the base station may include or be in communication with anmodel repository (MR) that is configured to store multiple machinelearning models and corresponding parameters. In some cases, a centralunit may alternatively be referred to as a centralized unit.

In some cases, the network may provide the UE with a neural networkmodel, an machine learning model, corresponding parameters, or anycombination thereof in response to a request from the UE to implementmachine learning. In some examples, the UE may download the neuralnetwork function, the machine learning model, or correspondingparameters via the user plane (e.g., directly from the MR). In otherexamples, the UE may download the neural network function, the machinelearning model, or corresponding parameters via the control plane (e.g.,obtain the model from the CU-CP). The messages exchanged between the UEand the network pertaining to machine learning (e.g., request message)may be signaled via radio resource control (RRC) (e.g., over existing ornew radio bearers, using new containers within RRC messages, or anycombination thereof). The techniques as described herein may enablemachine learning at a UE. Machine learning may allow a UE to performtasks with little or no instruction from the network. As such, enablingmachine learning at the UE may result in less signaling overhead andreduced power consumption at the UE.

Aspects of the disclosure are initially described in the context ofwireless communications systems. Additional aspects are described in thecontext of a protocol stack and process flows. Aspects of the disclosureare further illustrated by and described with reference to apparatusdiagrams, system diagrams, and flowcharts that relate to configuring aUE for machine learning.

FIG. 1 illustrates an example of a wireless communications system 100that supports configuring a UE for machine learning in accordance withaspects of the present disclosure. The wireless communications system100 may include one or more base stations 105, one or more UEs 115, anda core network 130. In some examples, the wireless communications system100 may be a Long Term Evolution (LTE) network, an LTE-Advanced (LTE-A)network, an LTE-A Pro network, or a New Radio (NR) network. In someexamples, the wireless communications system 100 may support enhancedbroadband communications, ultra-reliable communications, low latencycommunications, communications with low-cost and low-complexity devices,or any combination thereof.

The base stations 105 may be dispersed throughout a geographic area toform the wireless communications system 100 and may be devices indifferent forms or having different capabilities. The base stations 105and the UEs 115 may wirelessly communicate via one or more communicationlinks 125. Each base station 105 may provide a coverage area 110 overwhich the UEs 115 and the base station 105 may establish one or morecommunication links 125. The coverage area 110 may be an example of ageographic area over which a base station 105 and a UE 115 may supportthe communication of signals according to one or more radio accesstechnologies.

The UEs 115 may be dispersed throughout a coverage area 110 of thewireless communications system 100, and each UE 115 may be stationary,or mobile, or both at different times. The UEs 115 may be devices indifferent forms or having different capabilities. Some example UEs 115are illustrated in FIG. 1 . The UEs 115 described herein may be able tocommunicate with various types of devices, such as other UEs 115, thebase stations 105, or network equipment (e.g., core network nodes, relaydevices, integrated access and backhaul (IAB) nodes, or other networkequipment), as shown in FIG. 1 .

The base stations 105 may communicate with the core network 130, or withone another, or both. For example, the base stations 105 may interfacewith the core network 130 through one or more backhaul links 120 (e.g.,via an S1, N2, N3, or other interface). The base stations 105 maycommunicate with one another over the backhaul links 120 (e.g., via anX2, Xn, or other interface) either directly (e.g., directly between basestations 105), or indirectly (e.g., via core network 130), or both. Insome examples, the backhaul links 120 may be or include one or morewireless links.

One or more of the base stations 105 described herein may include or maybe referred to by a person having ordinary skill in the art as a basetransceiver station, a radio base station, an access point, a radiotransceiver, a NodeB, an eNodeB (eNB), a next-generation NodeB or agiga-NodeB (either of which may be referred to as a gNB), a Home NodeB,a Home eNodeB, or other suitable terminology.

A UE 115 may include or may be referred to as a mobile device, awireless device, a remote device, a handheld device, or a subscriberdevice, or some other suitable terminology, where the “device” may alsobe referred to as a unit, a station, a terminal, or a client, amongother examples. A UE 115 may also include or may be referred to as apersonal electronic device such as a cellular phone, a personal digitalassistant (PDA), a tablet computer, a laptop computer, or a personalcomputer. In some examples, a UE 115 may include or be referred to as awireless local loop (WLL) station, an Internet of Things (IoT) device,an Internet of Everything (IoE) device, or a machine type communications(MTC) device, among other examples, which may be implemented in variousobjects such as appliances, or vehicles, meters, among other examples.

The UEs 115 described herein may be able to communicate with varioustypes of devices, such as other UEs 115 that may sometimes act as relaysas well as the base stations 105 and the network equipment includingmacro eNBs or gNBs, small cell eNBs or gNBs, or relay base stations,among other examples, as shown in FIG. 1 .

The UEs 115 and the base stations 105 may wirelessly communicate withone another via one or more communication links 125 over one or morecarriers. The term “carrier” may refer to a set of radio frequencyspectrum resources having a defined physical layer structure forsupporting the communication links 125. For example, a carrier used fora communication link 125 may include a portion of a radio frequencyspectrum band (e.g., a bandwidth part (BWP)) that is operated accordingto one or more physical layer channels for a given radio accesstechnology (e.g., LTE, LTE-A, LTE-A Pro, NR). Each physical layerchannel may carry acquisition signaling (e.g., synchronization signals,system information), control signaling that coordinates operation forthe carrier, user data, or other signaling. The wireless communicationssystem 100 may support communication with a UE 115 using carrieraggregation or multi-carrier operation. A UE 115 may be configured withmultiple downlink component carriers and one or more uplink componentcarriers according to a carrier aggregation configuration. Carrieraggregation may be used with both frequency division duplexing (FDD) andtime division duplexing (TDD) component carriers.

Signal waveforms transmitted over a carrier may be made up of multiplesubcarriers (e.g., using multi-carrier modulation (MCM) techniques suchas orthogonal frequency division multiplexing (OFDM) or discrete Fouriertransform spread OFDM (DFT-S-OFDM)). In a system employing MCMtechniques, a resource element may include one symbol period (e.g., aduration of one modulation symbol) and one subcarrier, where the symbolperiod and subcarrier spacing are inversely related. The number of bitscarried by each resource element may depend on the modulation scheme(e.g., the order of the modulation scheme, the coding rate of themodulation scheme, or both). Thus, the more resource elements that a UE115 receives and the higher the order of the modulation scheme, thehigher the data rate may be for the UE 115. A wireless communicationsresource may refer to a combination of a radio frequency spectrumresource, a time resource, and a spatial resource (e.g., spatial layersor beams), and the use of multiple spatial layers may further increasethe data rate or data integrity for communications with a UE 115.

The time intervals for the base stations 105 or the UEs 115 may beexpressed in multiples of a basic time unit which may, for example,refer to a sampling period of T_(s)=1/(Δf_(max)·N_(f)) seconds, whereΔf_(max) may represent the maximum supported subcarrier spacing, andN_(f) may represent the maximum supported discrete Fourier transform(DFT) size. Time intervals of a communications resource may be organizedaccording to radio frames each having a specified duration (e.g., 10milliseconds (ms)). Each radio frame may be identified by a system framenumber (SFN) (e.g., ranging from 0 to 1023).

Each frame may include multiple consecutively numbered subframes orslots, and each subframe or slot may have the same duration. In someexamples, a frame may be divided (e.g., in the time domain) intosubframes, and each subframe may be further divided into a number ofslots. Alternatively, each frame may include a variable number of slots,and the number of slots may depend on subcarrier spacing. Each slot mayinclude a number of symbol periods (e.g., depending on the length of thecyclic prefix prepended to each symbol period). In some wirelesscommunications systems 100, a slot may further be divided into multiplemini-slots containing one or more symbols. Excluding the cyclic prefix,each symbol period may contain one or more (e.g., N_(f)) samplingperiods. The duration of a symbol period may depend on the subcarrierspacing or frequency band of operation.

A subframe, a slot, a mini-slot, or a symbol may be the smallestscheduling unit (e.g., in the time domain) of the wirelesscommunications system 100 and may be referred to as a transmission timeinterval (TTI). In some examples, the TTI duration (e.g., the number ofsymbol periods in a TTI) may be variable. Additionally or alternatively,the smallest scheduling unit of the wireless communications system 100may be dynamically selected (e.g., in bursts of shortened TTIs (sTTIs)).

Physical channels may be multiplexed on a carrier according to varioustechniques. A physical control channel and a physical data channel maybe multiplexed on a downlink carrier, for example, using one or more oftime division multiplexing (TDM) techniques, frequency divisionmultiplexing (FDM) techniques, or hybrid TDM-FDM techniques. A controlregion (e.g., a control resource set (CORESET)) for a physical controlchannel may be defined by a number of symbol periods and may extendacross the system bandwidth or a subset of the system bandwidth of thecarrier. One or more control regions (e.g., CORESETs) may be configuredfor a set of the UEs 115. For example, one or more of the UEs 115 maymonitor or search control regions for control information according toone or more search space sets, and each search space set may include oneor multiple control channel candidates in one or more aggregation levelsarranged in a cascaded manner. An aggregation level for a controlchannel candidate may refer to a number of control channel resources(e.g., control channel elements (CCEs)) associated with encodedinformation for a control information format having a given payloadsize. Search space sets may include common search space sets configuredfor sending control information to multiple UEs 115 and UE-specificsearch space sets for sending control information to a specific UE 115.

In some examples, a base station 105 may be movable and thereforeprovide communication coverage for a moving geographic coverage area110. In some examples, different geographic coverage areas 110associated with different technologies may overlap, but the differentgeographic coverage areas 110 may be supported by the same base station105. In other examples, the overlapping geographic coverage areas 110associated with different technologies may be supported by differentbase stations 105. The wireless communications system 100 may include,for example, a heterogeneous network in which different types of thebase stations 105 provide coverage for various geographic coverage areas110 using the same or different radio access technologies.

Some UEs 115 may be configured to employ operating modes that reducepower consumption, such as half-duplex communications (e.g., a mode thatsupports one-way communication via transmission or reception, but nottransmission and reception simultaneously). In some examples,half-duplex communications may be performed at a reduced peak rate.Other power conservation techniques for the UEs 115 include entering apower saving deep sleep mode when not engaging in active communications,operating over a limited bandwidth (e.g., according to narrowbandcommunications), or a combination of these techniques. For example, someUEs 115 may be configured for operation using a narrowband protocol typethat is associated with a defined portion or range (e.g., set ofsubcarriers or resource blocks (RBs)) within a carrier, within aguard-band of a carrier, or outside of a carrier.

The wireless communications system 100 may be configured to supportultra-reliable communications or low-latency communications, or variouscombinations thereof. For example, the wireless communications system100 may be configured to support ultra-reliable low-latencycommunications (URLLC). The UEs 115 may be designed to supportultra-reliable, low-latency, or critical functions. Ultra-reliablecommunications may include private communication or group communicationand may be supported by one or more services such as push-to-talk,video, or data. Support for ultra-reliable, low-latency functions mayinclude prioritization of services, and such services may be used forpublic safety or general commercial applications. The termsultra-reliable, low-latency, and ultra-reliable low-latency may be usedinterchangeably herein.

In some examples, a UE 115 may also be able to communicate directly withother UEs 115 over a device-to-device (D2D) communication link 135(e.g., using a peer-to-peer (P2P) or D2D protocol). One or more UEs 115utilizing D2D communications may be within the geographic coverage area110 of a base station 105. Other UEs 115 in such a group may be outsidethe geographic coverage area 110 of a base station 105 or be otherwiseunable to receive transmissions from a base station 105. In someexamples, groups of the UEs 115 communicating via D2D communications mayutilize a one-to-many (1:M) system in which each UE 115 transmits toevery other UE 115 in the group. In some examples, a base station 105facilitates the scheduling of resources for D2D communications. In othercases, D2D communications are carried out between the UEs 115 withoutthe involvement of a base station 105.

The core network 130 may provide user authentication, accessauthorization, tracking, Internet Protocol (IP) connectivity, and otheraccess, routing, or mobility functions. The core network 130 may be anevolved packet core (EPC) or 5G core (5GC), which may include at leastone control plane entity that manages access and mobility (e.g., amobility management entity (MME), an access and mobility managementfunction (AMF)) and at least one user plane entity that routes packetsor interconnects to external networks (e.g., a serving gateway (S-GW), aPacket Data Network (PDN) gateway (P-GW), or a user plane function(UPF)). The control plane entity may manage non-access stratum (NAS)functions such as mobility, authentication, and bearer management forthe UEs 115 served by the base stations 105 associated with the corenetwork 130. User IP packets may be transferred through the user planeentity, which may provide IP address allocation as well as otherfunctions. The user plane entity may be connected to IP services 150 forone or more network operators. The IP services 150 may include access tothe Internet, Intranet(s), an IP Multimedia Subsystem (IMS), or aPacket-Switched Streaming Service.

Some of the network devices, such as a base station 105, may includesubcomponents such as an access network entity 140, which may be anexample of an access node controller (ANC). Each access network entity140 may communicate with the UEs 115 through one or more other accessnetwork transmission entities 145, which may be referred to as radioheads, smart radio heads, or transmission/reception points (TRPs). Eachaccess network transmission entity 145 may include one or more antennapanels. In some configurations, various functions of each access networkentity 140 or base station 105 may be distributed across various networkdevices (e.g., radio heads and ANCs) or consolidated into a singlenetwork device (e.g., a base station 105).

The wireless communications system 100 may operate using one or morefrequency bands, for example in the range of 300 megahertz (MHz) to 300gigahertz (GHz). Generally, the region from 300 MHz to 3 GHz is known asthe ultra-high frequency (UHF) region or decimeter band because thewavelengths range from approximately one decimeter to one meter inlength. The UHF waves may be blocked or redirected by buildings andenvironmental features, but the waves may penetrate structuressufficiently for a macro cell to provide service to the UEs 115 locatedindoors. The transmission of UHF waves may be associated with smallerantennas and shorter ranges (e.g., less than 100 kilometers) compared totransmission using the smaller frequencies and longer waves of the highfrequency (HF) or very high frequency (VHF) portion of the spectrumbelow 300 MHz.

The wireless communications system 100 may utilize both licensed andunlicensed radio frequency spectrum bands. For example, the wirelesscommunications system 100 may employ License Assisted Access (LAA),LTE-Unlicensed (LTE-U) radio access technology, or NR technology in anunlicensed band such as the 5 GHz industrial, scientific, and medical(ISM) band. When operating in unlicensed radio frequency spectrum bands,devices such as the base stations 105 and the UEs 115 may employ carriersensing for collision detection and avoidance. In some examples,operations in unlicensed bands may be based on a carrier aggregationconfiguration in conjunction with component carriers operating in alicensed band (e.g., LAA). Operations in unlicensed spectrum may includedownlink transmissions, uplink transmissions, P2P transmissions, or D2Dtransmissions, among other examples.

A base station 105 or a UE 115 may be equipped with multiple antennas,which may be used to employ techniques such as transmit diversity,receive diversity, multiple-input multiple-output (MIMO) communications,or beamforming. The antennas of a base station 105 or a UE 115 may belocated within one or more antenna arrays or antenna panels, which maysupport MIMO operations or transmit or receive beamforming. For example,one or more base station antennas or antenna arrays may be co-located atan antenna assembly, such as an antenna tower. In some examples,antennas or antenna arrays associated with a base station 105 may belocated in diverse geographic locations. A base station 105 may have anantenna array with a number of rows and columns of antenna ports thatthe base station 105 may use to support beamforming of communicationswith a UE 115. Likewise, a UE 115 may have one or more antenna arraysthat may support various MIMO or beamforming operations. Additionally oralternatively, an antenna panel may support radio frequency beamformingfor a signal transmitted via an antenna port.

The base stations 105 or the UEs 115 may use MIMO communications toexploit multipath signal propagation and increase the spectralefficiency by transmitting or receiving multiple signals via differentspatial layers. Such techniques may be referred to as spatialmultiplexing. The multiple signals may, for example, be transmitted bythe transmitting device via different antennas or different combinationsof antennas. Likewise, the multiple signals may be received by thereceiving device via different antennas or different combinations ofantennas. Each of the multiple signals may be referred to as a separatespatial stream and may carry bits associated with the same data stream(e.g., the same codeword) or different data streams (e.g., differentcodewords). Different spatial layers may be associated with differentantenna ports used for channel measurement and reporting. MIMOtechniques include single-user MIMO (SU-MIMO), where multiple spatiallayers are transmitted to the same receiving device, and multiple-userMIMO (MU-MIMO), where multiple spatial layers are transmitted tomultiple devices.

Beamforming, which may also be referred to as spatial filtering,directional transmission, or directional reception, is a signalprocessing technique that may be used at a transmitting device or areceiving device (e.g., a base station 105, a UE 115) to shape or steeran antenna beam (e.g., a transmit beam, a receive beam) along a spatialpath between the transmitting device and the receiving device.Beamforming may be achieved by combining the signals communicated viaantenna elements of an antenna array such that some signals propagatingat particular orientations with respect to an antenna array experienceconstructive interference while others experience destructiveinterference. The adjustment of signals communicated via the antennaelements may include a transmitting device or a receiving deviceapplying amplitude offsets, phase offsets, or both to signals carriedvia the antenna elements associated with the device. The adjustmentsassociated with each of the antenna elements may be defined by abeamforming weight set associated with a particular orientation (e.g.,with respect to the antenna array of the transmitting device orreceiving device, or with respect to some other orientation).

The wireless communications system 100 may be a packet-based networkthat operates according to a layered protocol stack. In the user plane,communications at the bearer or Packet Data Convergence Protocol (PDCP)layer may be IP-based. A Radio Link Control (RLC) layer may performpacket segmentation and reassembly to communicate over logical channels.A Medium Access Control (MAC) layer may perform priority handling andmultiplexing of logical channels into transport channels. The MAC layermay also use error detection techniques, error correction techniques, orboth to support retransmissions at the MAC layer to improve linkefficiency. In the control plane, the RRC protocol layer may provideestablishment, configuration, and maintenance of an RRC connectionbetween a UE 115 and a base station 105 or a core network 130 supportingradio bearers for user plane data. At the physical layer, transportchannels may be mapped to physical channels.

In some examples, a network may configure the UE 115 for machinelearning. The UE 115 may transmit capability information to the network(e.g., the base station 105). The capability information may include oneor more of a list of potential neural network functions, a list ofpotential machine learning models, or an indication of whether or notthe UE 115 may request machine learning. Based on the capabilityinformation, the network may select a set of neural network functions, aset of machine learning models, and sets of corresponding parameters andindicate them to the UE 115. In some examples, the UE 115 may send amessage to the network requesting to implement machine learning (e.g.,based on some trigger). The message may include an indication of aneural network function, a neural network model, and a correspondingparameter set. In response to the request message, the network mayconfigure the machine learning model and the corresponding parameters atthe UE 115. When the UE 115 obtains the machine learning model and thecorresponding parameter set, the network may activate machine learningat the UE 115 and the UE 115 utilize machine learning to perform one ormore tasks. The techniques as described herein may enable machinelearning at a UE 115. Machine learning may allow a UE 115 to performtasks with little or no instruction from the network. As such, enablingmachine learning at the UE may result in less signaling overhead andreduced power consumption at the UE 115.

FIG. 2A illustrates an example of a wireless communications system 201that supports configuring a UE for machine learning in accordance withaspects of the present disclosure. In some examples, the wirelesscommunications system 201 may implement or be implemented by a wirelesscommunications system 100. For example, the wireless communicationssystem 201 may include a base station 105-a and a UE 115-a which may beexamples of a base station 105 and a UE 115 as described with referenceto FIG. 1 .

FIG. 2B illustrates an example of a protocol stack 202 that supportsconfiguring a UE for machine learning as described herein. In someexamples, the protocol stack 202 may implement or be implemented by awireless communications system 100. For example, the protocol stack 202may be implemented by a base station 105 and a UE 115 as described withreference to FIG. 1 .

In some examples, the wireless communications system 201 may supportmachine learning or artificial intelligence. Using machine learning,devices (e.g., the base station 105-a or the UE 115-a) may perform taskswithout being explicitly programmed to do so. In order to performmachine learning, the device may obtain a neural network function and aneural network model. The neural network function may be defined as afunction supported by one or more neural network models and may bespecific to the task being performed. The inputs and outputs of eachneural network function may be set (e.g., standardized) and each neuralnetwork function may be identified by a neural network functionidentifier (ID). The neural network model may be defined as a modelstructure and a parameter set. The model structure may be identified bya model ID and each model ID may be associated with a neural networkfunction. The model ID may also specify the set of parameterscorresponding to the neural network model. The set of parameters mayinclude the weights of the neural network model and other configurationparameters. In some examples, the UE 115-a may utilize machine learningfor cell reselection, beam management, etc. However, methods forconfiguring a UE 115-a for machine learning may be lacking orinefficient.

In some examples, a base station 105 may include different networkentities. For example, the base station 105-a may include at least aCU-UP 205, a CU-CP 210, a DU 220, and a radio unit (RU) 225. The CU-CP210 may host the control plane part of the packet data convergenceprotocol (PDCP) and the CU-UP 205 may host the user plane part of thePDCP. The DU 220, on the other hand, may support lower layer signaling(e.g., medium access control (MAC) protocol or radio link control (RLC)protocol) and the RU 225 may support physical layer signaling as well asdigital beamforming functionality. The CU-UP 205 may be connected to theCU-CP 210 via an E1 interface. Moreover, the CU-UP 205 may be connectedto the DU 220 via an F1-U interface and the CU-CP 210 may be connectedto the DU 220 via an F1-C interface. To support machine learning at theUE 115-a as described herein, the base station 105-a may also include aCU-XP 215. The CU-XP 215 may host the machine learning control (MLC)protocol as shown in FIG. 2B. The MLC protocol may define the controlplane messaging for managing machine learning or artificial intelligenceat the network. In some examples, the CU-XP 215 may be connected to theCU-UP via an E3 interface. Moreover, the base station 105-a may be incommunication with a UE model repository (UE-MR) 230. The UE-MR 230 maybe defined as a central location in which the neural network models arestored (e.g., cloud storage, online storage, etc.).

To implement machine learning at the UE 115-a, MLC messages may beexchanged between the CU-XP 215 and the UE 115-a. MLC messages may bedefined as control messages that facilitate machine learning orartificial intelligence. In some examples, MLC messages may be exchangedbetween the UE 115-a and the CU-XP 215 via RRC signaling. For example,the UE 115-a and the CU-CP 210 may exchange RRC signaling that includesa container that carries an MLC message intended for the CU-XP 215. TheCU-CP 210 may then forward the MLC message to the CU-XP 215. The RRCcontainer may be decoded at the CU-XP 215 and sent to the MLC layer. Insome examples, the MLC message may be carried over signaling radiobearer (SRB) 2 in RRC. In another example, a new SRB may be defined formachine learning (e.g., SRB X) and the MLC message may be carried overthe newly defined SRB in RRC. In some examples, the MLC message can bepiggybacked by existing RRC messages. For example, the MLC message maybe included in an RRC reconfiguration message, an RRC reconfigurationcomplete message, or an RRC setup/resume complete message as acontainer.

FIG. 3 illustrates an example of a process flow 300 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. In some examples, the process flow 300 may beimplemented by aspects of a wireless communications system 100 and awireless communications system 201. For example, the process flow 300may be performed by a UE 115-b, a CU-CP 305, a CU-XP 310, and a UE-MR320 which may be examples of a UE 115, a CU-CP 210, a CU-XP 215, and aUE-MR 230 as described with reference to FIG. 2 . The process flow 300may support a network configuring the UE 115-b for machine learning.Alternative examples of the following may be implemented, where somesteps are performed in a different order then described or are notperformed at all. In some cases, steps may include additional featuresnot mentioned below, or further steps may be added.

At 325, the UE 115-b may exchange capability information related tomachine learning with the network. In some examples, the CU-CP 305 maysend a message to the UE 115-b enquiring about the capabilityinformation and the UE 115-b may send the capability information to theCU-CP 305 based on receiving the message enquiring about the capabilityinformation. Upon receiving the capability information from the UE115-b, the CU-CP 305 may forward the capability information to the CU-XP310. In some examples, the capability information may include a list ofneural network functions supported by the UE 115-b, a list of neuralnetwork models (e.g., a list of model IDs) supported by the UE 115-b, anindication of whether or not the UE 115-b may request to be configuredfor machine learning, etc. Based on the capability information, theCU-CP 305 or the CU-XP 310 may determine one or more neural networkfunctions from the list of neural network functions, one or more modelIDs from the list of model IDs, and IDs of corresponding sets ofparameters. An indication of the one or more neural network functions,the one or more model IDs, and the corresponding parameter set IDs maybe sent to the UE 115-b from the CU-CP 305 in a MLC container as part ofan RRC message (e.g., an RRC reconfiguration message). To confirmreceipt of the RRC message and the MLC container, the UE 115-b maytransmit an RRC message (e.g., an RRC reconfiguration complete message)to the CU-CP 305.

In some examples, the UE 115-b may request to implement machine learning(perform machine learning for a specific task). In such example, the RRCmessage to the UE 115-b to from the CU-CP 305 may also include aprohibit timer for the machine learning request. The prohibit timer maylimit the number of times that the UE 115-b may send a machine learningrequest to the network. Additionally or alternatively, the RRC messagemay include an indication of a blacklist of neural network functions,model IDs, and corresponding parameter set IDs and a whitelist of neuralnetwork functions, model IDs, and corresponding parameter set IDs The UE115-b may be able to request to implement models in the whitelist, butmay be unable to request to implement models in the blacklist. Eachmodel ID of the one or more model IDs indicated to the UE 115-b at 325may be associated with a condition (or applicable scope). The conditionmay be a location (e.g., a cell, a cell list, tracking area identity(TAI), radio access network notification area (RNA), multi-broadcastsingle-frequency network (MBSFN) area, or a geographical area), anetwork slice, a deep neural network (DNN) type, a public land mobilenetwork (PLMN) list (e.g., a list of public network integratednon-public network (PNI-NPN) IDs or standalone nonpublic network (SNPN)IDs), a type of UE, RRC states, a type of service (e.g., multi-broadcastservice (MBS) or sidelink), or a configuration (e.g., MIMO, dualconnectivity/carrier aggregation (DC/CA), or mmW).

At 330, the UE 115-b may potentially determine whether a conditionassociated with a model ID of the one or more model IDs is satisfied.For example, the UE 115-b may determine that the UE 115-b has left acell associated with a first model ID and entered a cell associated witha second model ID. When the UE 115-b determines that the conditionassociated with a model ID is satisfied, the UE 115-b may send a machinelearning request message to the CU-CP 305 at 335. The machine learningrequest message may include one or more of the model ID whose conditionwas satisfied (e.g., the second model ID), a neural network function ofthe one or more neural network functions, or an indication of thecondition that was satisfied. In some examples, the UE 115-b may includethe machine learning request in a UE assistance information message.More specifically, a machine learning assistance information elementincluding the information of the machine learning request message mayadded to the UE assistance information. In some examples, if the modelswhose condition is satisfied is included in the blacklist, the UE 115-bmay not send the machine learning request.

Upon receiving the machine learning request from the UE 115-b, thenetwork (e.g., CU-XP 310 or CU-CP 305) may select a neural networkfunction (e.g., from the one or more neural network functions indicatedin the machine learning request message received at 335) and a machinelearning model (e.g., select a machine learning model corresponding tothe model ID indicated in the machine learning request message receivedat 335) and configure the UE 115-b with the machine learning model aswell as a corresponding set of parameters at 340. In some examples,configuring the UE 115-b with the neural network model may includedownloading the neural network model from the UE-MR 320. The differentaspects of neural network model download and upload are discussed inmore detail in FIGS. 4-6 .

In addition to the UE 115-b, other network nodes 315 (e.g., distributedunit, CU-UP, or RIC) may be configured with the selected machinelearning model and corresponding set of parameters. To configure theother network nodes 315, the CU-XP 310 may send a model setup requestmessage to the other network nodes 315, where the model setup requestmessage may include a model ID of the selected neural network model andcorresponding parameter set ID. The other network nodes 315 may send themodel ID and the parameter set ID to the MDAC via a model queryingrequest message and the MDAC may transmit a model querying response tothe other network nodes including an address (e.g., a web address or aURL) corresponding to the model ID and an address corresponding to theparameter ID. Upon receiving the model querying response message, theother network nodes 315 may download the neural network model associatedwith the model ID and the parameter set associated with the parameterset ID by their respective web addresses from the UE-MR 320. The othernetwork nodes 315 may then confirm configuration of the neural networkmodel and corresponding parameter set via a model setup response messageto the CU-XP 310 and the CU-XP 310 may forward the confirmation of theneural network model configuration via a neural network response messageto the CU-CP 305.

At 345, the network may activate the neural network model. To activatethe neural network model at the UE 115-b, the UE 115-b may transmit amodel activation request message to the other network nodes 315 via theCU-CP 305 requesting activation of machine learning and the othernetwork nodes may send a model activation response message to the UE115-b via a MAC-CE or RRC signaling activating the machine learning atthe UE 115-b. To activate the neural network model at the network, theCU-CP 305 may send a model activation message to the CU-XP 310 and theCU-XP 310 may send the model activation message to the other networknodes 315 activating machine learning at the other network nodes 315.

FIG. 4 illustrates an example of a process flow 400 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. In some examples, the process flow 400 may beimplemented by aspects of a wireless communications system 100, awireless communications system 201, and a process flow 300. For example,the process flow 400 may be performed by a UE 115-c and a UE-MR 405which may be examples of a UE 115 and a UE-MR 230 as described withreference to FIG. 2 . The process flow 400 may support the upload anddownload of neural network models and parameter sets at the UE 115-c.Alternative examples of the following may be implemented, where somesteps are performed in a different order then described or are notperformed at all. In some cases, steps may include additional featuresnot mentioned below, or further steps may be added.

As described with reference to FIG. 3 , the network (e.g., a CU-CP or aCU-XP) may select a neural network function, a neural network model, anda corresponding set of parameters (e.g., based on a capability of the UE115-c or based on a request message from the UE 115-c) and indicate theneural network function, the neural network model, and the correspondingset of parameters to the UE 115-c such that the UE 115-c may performmachine learning. For example, the network may transmit message (e.g.,an RRC reconfiguration message) including a neural network function ID,a model ID, and a corresponding parameter set ID. The UE 115-c may thenperform the following procedure to download the indicated neural networkmodel and the corresponding set of parameters.

At 410, the UE 115-c may construct an address (e.g., a web address or aURL) of the neural network model and an address (e.g., a web address ora URL) of the set of parameters. In some examples, the UE 115-c mayconstruct the addresses based on the model ID and the parameters set ID.The model ID and the parameter set ID may act as input for a predefinedrule.

At 415, the UE 115-c may download the model and the corresponding set ofparameters from the UE-MR 405 by the addresses. The UE 115-c may sendthe address of the neural network model and in some examples, theaddress of the set of parameters to the UE-MR 405. For example, the UE115-c may send an HTTP GET message including the address of the neuralnetwork model and an HTTP GET message including the address of the setof parameters. Upon receiving the addresses, the UE-MR 405 may send theneural network model (e.g., in a 200 GET message) and the correspondingset of parameters (e.g., in a 200 GET message) to the UE 115-c. That is,the UE 115-c may download the neural network model and the set ofparameters from the UE-MR 405 by their respective addresses. In someexamples, the UE 115-c may cache frequently used neural network modelsand parameter sets. In some examples, a version tag and a timer may beused to evaluate and guard the freshness of the cached neural networkmodels and parameter sets. If the neural network model and the parameterset are cached locally, the UE 115-c may not download the neural networkmodel and the parameter set from the UE-MR 405, but instead obtain theneural network model and the parameter set from the cached data.

In some examples, the UE 115-c may not obtain the neural network modeland the set of parameters from the UE-MR 405, but may obtain the neuralnetwork model and the set of parameters elsewhere (e.g., using a modeltraining configuration). In such example, the UE 115-c may upload theneural network model and the set of parameters to the UE-MR 405 suchthat the UE-MR 405 may store the neural network models and the set ofparameters for future use or such that other devices may access theneural network model and the set of parameters.

In some examples, at 420, the UE 115-c may construct an address (e.g., aweb address or a URL) of the set of parameters. In another example, theUE 115-c may construct an address (e.g., a web address or a URL) forboth the neural network model and the set of parameters. The UE 115-cmay know the address from the training configuration or the UE 115-c maydetermine the address using a predefined rule where the neural networkmodel ID associated with the neural network model and the parameter setID associated with the set of parameters are used as inputs.

At 425, the UE 115-c may upload the parameter set and, in some examples,the neural network model to the UE-MR 405 by the address. In someexamples, the UE 115-c may upload the set of parameters or the neuralnetwork model to the UE-MR 405 by sending a HTTP PUT message to theUE-MR 405 including the neural network model and the set of parameters.To confirm receipt of the set of parameters or the neural network model,the UE-MR may send a confirmation message (e.g., a 200 OK message) tothe UE 115-c.

FIG. 5 illustrates an example of a process flow 500 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. In some examples, the process flow 500 may beimplemented by aspects of a wireless communications system 100, awireless communications system 201, a process flow 300, or a processflow 400. For example, the process flow 500 may be performed by a UE115-d, a CU-CP 505, a CU-XP 510, and a UE-MR 515 which may be examplesof a UE 115, a CU-CP 210, a CU-XP 215, and a UE-MR 230 as described withreference to FIG. 2 . The process flow 500 may support the upload anddownload of neural network models and parameter sets at the UE 115-d.Alternative examples of the following may be implemented, where somesteps are performed in a different order then described or are notperformed at all. In some cases, steps may include additional featuresnot mentioned below, or further steps may be added.

As described with reference to FIG. 3 , the network (e.g., a CU-CP 505or a CU-XP 510) may select a neural network function, a neural networkmodel, and a corresponding set of parameters (e.g., based on acapability of the UE 115-d or based on a request message from the UE115-d) and indicate the neural network function, the neural networkmodel, and the corresponding set of parameters to the UE 115-d such thatthe UE 115-d may perform machine learning. For example, the network maytransmit message (e.g., an RRC reconfiguration message) including aneural network function ID, a model ID, and a corresponding parameterset ID. The UE 115-d may then perform the following procedure to obtainthe indicated neural network model and the corresponding set ofparameters.

At 520, the UE 115-d may obtain the neural network model and thecorresponding set of parameters from the network. First, the UE 115-dmay send a model download request message to the CU-CP 505 at 525. Themodel download request message may include the model ID corresponding tothe neural network model and the parameter set ID corresponding to theset of parameters. In some examples, the UE 115-d may transmit thedownload request message to the CU-CP 505 via RRC signaling over SRB 2.

At 530, the CU-CP 505 may forward the model download request to theCU-XP 510. The CU-XP 510 may then retrieve the neural network model andthe set of parameters from the UE-MR 515 at 535. Similar to how the UE115 downloads the neural network model and the set of parameters in FIG.4 , the CU-XP 510 may construct addresses for the neural network modeland the set of parameters using the model ID and the parameter set IDand download the neural network model and the set of parameters usingthe addresses from the UE-MR 515. Alternatively, the CU-XP 510 mayobtain the addresses from another network entity (e.g., MDAC).

At 540, the CU-XP 510 may transmit a model download response message tothe CU-CP 505. The model download model response message may include theneural network model and the set of parameters.

At 545, the CU-CP 505 may forward the model download response message tothe UE 115-d. In some example, the CU-CP 505 may send the model downloadresponse message to the UE 115-d via RRC signaling over a new SRB (e.g.,SRB X). If the size of the neural network model and the set ofparameters is above a threshold, RRC segmentation may be used to sendthe neural network model and the set of parameters to the UE 115-d.

In some examples, the UE 115-d may not obtain the neural network modeland the set of parameters from the UE-MR 515, but may obtain the neuralnetwork model and the set of parameters elsewhere (e.g., using a modeltraining configuration). In such example, the UE 115-d may upload theneural network model and the set of parameters to the UE-MR 515 suchthat the UE-MR 515 may store the neural network models and the set ofparameters for future use or such that other devices may access theneural network model and the set of parameters.

At 550, the UE 115-d may upload the neural network model and the set ofparameters to the UE-MR 515 via one or more network nodes. For example,the UE 115-d may send a model upload request message to the CU-CP 505 at555. The model upload request message may include the neural networkmodel and the set of parameters. In some examples, the UE 115-d maytransmit the model upload request message via RRC signaling over SRB 2.

At 560, the CU-CP 505 may forward the model upload request to the CU-XP510. The CU-XP 510 may then upload the neural network model and the setof parameters to the UE-MR 515 at 565. Similar to how the UE 115 uploadsthe neural network model and the set of parameter set in FIG. 4 , theCU-XP 510 may construct addresses for the neural network models and theset of parameters using the model ID and parameter set ID and upload theneural network model and the set of parameters using the addresses tothe UE-MR 515. Alternatively, the CU-XP 510 may obtain the addressesfrom another network entity (e.g., MDAC).

At 570, the CU-XP 510 may transmit a model upload response message tothe CU-CP 505. The model download model response message may serve as aconfirmation that the neural network model and the set of parameterswere uploaded to the UE-MR 515.

At 575, the CU-CP 505 may forward the model download response message tothe UE 115-d. In some example, the CU-CP 505 may send the model downloadresponse message to the UE 115-d via RRC signaling over a new SRB (e.g.,SRB X).

FIG. 6 illustrates an example of a process flow 600 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. In some examples, the process flow 600 may beimplemented by aspects of a wireless communications system 100, awireless communications system 201, a process flow 300, a process flow400, or a process flow 500. For example, the process flow 500 may beperformed by a UE 115-e, a CU-XP 605, and a UE-MR 615 which may beexamples of a UE 115, a CU-XP 215, and a UE-MR 230 as described withreference to FIG. 2 . The process flow 600 may support the upload anddownload of neural network models and parameter sets at the UE 115-e.Alternative examples of the following may be implemented, where somesteps are performed in a different order then described or are notperformed at all. In some cases, steps may include additional featuresnot mentioned below, or further steps may be added.

As described with reference to FIG. 3 , the network (e.g., a CU-CP or aCU-XP 605) may select a neural network function, a neural network model,and a corresponding set of parameters (e.g., based on a capability ofthe UE 115-e or based on a request message from the UE 115-e) andindicate the neural network function, the neural network model, and thecorresponding set of parameters to the UE 115-e such that the UE 115-emay perform machine learning. For example, the network may transmitmessage (e.g., an RRC reconfiguration message) including a neuralnetwork function ID, a model ID, and a corresponding parameter set ID.The UE 115-e may then perform the following procedure to obtain theindicated neural network model and the corresponding set of parameters.

At 620, the UE 115-e may obtain the neural network model and thecorresponding set of parameters from the network. First, the UE 115-emay send a model query request message to the CU-XP 605 at 625. Themodel query request message may include the model ID corresponding tothe neural network model and the parameter set ID corresponding to theset of parameters.

At 630, the CU-XP 605 may forward the model query request message to theMDAC 610. In response to the model query request message, the MDAC 610may send a model query response message to the CU-XP 605 at 635. Themodel query request message may include an address for the neuralnetwork model and an address for the set of parameters.

At 640, the CU-XP 605 may forward the model query response message tothe UE 115-e. The UE 115-e may then send a message (e.g., a HTTP GETmessage) to the UE-MR including the address for the neural network modeland the address for the set of parameters at 645. The UE-MR 615 mayreceive the message and send the neural network model and the set ofparameters to the UE 115-e at 650. That is, the UE 115-e may downloadthe neural network model and the set of parameters by the address fromthe UE-MR 615.

In some examples, the UE 115-e may not obtain the neural network modeland the set of parameters from the UE-MR 615, but may obtain the neuralnetwork model and the set of parameters elsewhere (e.g., using a modeltraining configuration). In such example, the UE 115-e may upload theneural network model and the set of parameters to the UE-MR 615 suchthat the UE-MR 615 may store the neural network models and the set ofparameters for future use or such that other devices may access theneural network model and the set of parameters.

At 655, the UE 115-e may upload the neural network model and the set ofparameters to the UE-MR 615 via one or more network nodes. For example,the UE 115-e may send a model query request message to the CU-XP 605 at660. The model query request message may include the model ID associatedwith the neural network model and the parameter set ID associated withthe set of parameters.

At 665, the CU-XP 510 may forward the model query request to the MDAC610. In response to the model query request message, the MDAC 610 maysend a model query response message to the CU-XP 605 at 670. The modelquery response message may include an address for the neural networkmodel and an address for the set of parameters.

At 675, the CU-XP 605 may forward the model query response message tothe UE 115-e. The UE 115-e may then send a message (e.g., a HTTP PUTmessage) to the UE-MR 615 including the neural network model and the setof parameters at 680. The UE-MR 615 may receive the message and amessage confirming the upload of the neural network model and the set ofparameters to the UE-MR 615 at 685. That is, the UE 115-e may upload theneural network model and the set of parameters by the address to theUE-MR 615.

FIG. 7 shows a block diagram 700 of a device 705 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The device 705 may be an example of aspects of a UE115 as described herein. The device 705 may include a receiver 710, atransmitter 715, and a communications manager 720. The device 705 mayalso include a processor. Each of these components may be incommunication with one another (e.g., via one or more buses).

The receiver 710 may provide a means for receiving information such aspackets, user data, control information, or any combination thereofassociated with various information channels (e.g., control channels,data channels, information channels related to configuring a UE formachine learning). Information may be passed on to other components ofthe device 705. The receiver 710 may utilize a single antenna or a setof multiple antennas.

The transmitter 715 may provide a means for transmitting signalsgenerated by other components of the device 705. For example, thetransmitter 715 may transmit information such as packets, user data,control information, or any combination thereof associated with variousinformation channels (e.g., control channels, data channels, informationchannels related to configuring a UE for machine learning). In someexamples, the transmitter 715 may be co-located with a receiver 710 in atransceiver module. The transmitter 715 may utilize a single antenna ora set of multiple antennas.

The communications manager 720, the receiver 710, the transmitter 715,or various combinations thereof or various components thereof may beexamples of means for performing various aspects of configuring a UE formachine learning as described herein. For example, the communicationsmanager 720, the receiver 710, the transmitter 715, or variouscombinations or components thereof may support a method for performingone or more of the functions described herein.

In some examples, the communications manager 720, the receiver 710, thetransmitter 715, or various combinations or components thereof may beimplemented in hardware (e.g., in communications management circuitry).The hardware may include a processor, a digital signal processor (DSP),an application-specific integrated circuit (ASIC), a field-programmablegate array (FPGA) or other programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof configured as or otherwise supporting a means for performing thefunctions described in the present disclosure. In some examples, aprocessor and memory coupled with the processor may be configured toperform one or more of the functions described herein (e.g., byexecuting, by the processor, instructions stored in the memory).

Additionally or alternatively, in some examples, the communicationsmanager 720, the receiver 710, the transmitter 715, or variouscombinations or components thereof may be implemented in code (e.g., ascommunications management software or firmware) executed by a processor.If implemented in code executed by a processor, the functions of thecommunications manager 720, the receiver 710, the transmitter 715, orvarious combinations or components thereof may be performed by ageneral-purpose processor, a DSP, a central processing unit (CPU), anASIC, an FPGA, or any combination of these or other programmable logicdevices (e.g., configured as or otherwise supporting a means forperforming the functions described in the present disclosure).

In some examples, the communications manager 720 may be configured toperform various operations (e.g., receiving, monitoring, transmitting)using or otherwise in cooperation with the receiver 710, the transmitter715, or both. For example, the communications manager 720 may receiveinformation from the receiver 710, send information to the transmitter715, or be integrated in combination with the receiver 710, thetransmitter 715, or both to receive information, transmit information,or perform various other operations as described herein.

The communications manager 720 may support wireless communication at aUE in accordance with examples as disclosed herein. For example, thecommunications manager 720 may be configured as or otherwise support ameans for receiving a machine learning model of one or more machinelearning models, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to a neural networkfunction of one or more neural network functions, where the one or moremachine learning models, the one or more neural network functions, orany combination thereof may be associated with a machine learning MRthat is included in or coupled with a base station. The communicationsmanager 720 may be configured as or otherwise support a means forreceiving, from the base station, an activation message for the machinelearning model, the neural network function, or both.

By including or configuring the communications manager 720 in accordancewith examples as described herein, the device 705 (e.g., a processorcontrolling or otherwise coupled to the receiver 710, the transmitter715, the communications manager 720, or a combination thereof) maysupport techniques for reduced power consumption. The method asdescribed herein may allow a device 705 to utilize machine learning forsome communication procedures. Machine learning may allow the device 705to perform the communications without explicit programming which mayreduce power consumption at the UE.

FIG. 8 shows a block diagram 800 of a device 805 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The device 805 may be an example of aspects of adevice 705 or a UE 115 as described herein. The device 805 may include areceiver 810, a transmitter 815, and a communications manager 820. Thedevice 805 may also include a processor. Each of these components may bein communication with one another (e.g., via one or more buses).

The receiver 810 may provide a means for receiving information such aspackets, user data, control information, or any combination thereofassociated with various information channels (e.g., control channels,data channels, information channels related to configuring a UE formachine learning). Information may be passed on to other components ofthe device 805. The receiver 810 may utilize a single antenna or a setof multiple antennas.

The transmitter 815 may provide a means for transmitting signalsgenerated by other components of the device 805. For example, thetransmitter 815 may transmit information such as packets, user data,control information, or any combination thereof associated with variousinformation channels (e.g., control channels, data channels, informationchannels related to configuring a UE for machine learning). In someexamples, the transmitter 815 may be co-located with a receiver 810 in atransceiver module. The transmitter 815 may utilize a single antenna ora set of multiple antennas.

The device 805, or various components thereof, may be an example ofmeans for performing various aspects of configuring a UE for machinelearning as described herein. For example, the communications manager820 may include a UE machine learning manager 830, a UE activationcomponent 835, or any combination thereof. The communications manager820 may be an example of aspects of a communications manager 720 asdescribed herein. In some examples, the communications manager 820, orvarious components thereof, may be configured to perform variousoperations (e.g., receiving, monitoring, transmitting) using orotherwise in cooperation with the receiver 810, the transmitter 815, orboth. For example, the communications manager 820 may receiveinformation from the receiver 810, send information to the transmitter815, or be integrated in combination with the receiver 810, thetransmitter 815, or both to receive information, transmit information,or perform various other operations as described herein.

The communications manager 820 may support wireless communication at aUE in accordance with examples as disclosed herein. The UE machinelearning manager 830 may be configured as or otherwise support a meansfor receiving a machine learning model of one or more machine learningmodels, a set of parameters corresponding to the machine learning model,or a configuration corresponding to a neural network function of one ormore neural network functions, where the one or more machine learningmodels, the one or more neural network functions, or any combinationthereof may be associated with a machine learning MR that is included inor coupled with a base station. The UE activation component 835 may beconfigured as or otherwise support a means for receiving, from the basestation, an activation message for the machine learning model, theneural network function, or both.

FIG. 9 shows a block diagram 900 of a communications manager 920 thatsupports configuring a UE for machine learning in accordance withaspects of the present disclosure. The communications manager 920 may bean example of aspects of a communications manager 720, a communicationsmanager 820, or both, as described herein. The communications manager920, or various components thereof, may be an example of means forperforming various aspects of configuring a UE for machine learning asdescribed herein. For example, the communications manager 920 mayinclude a UE machine learning manager 930, a UE activation component935, a UE address manager 940, a UE upload component 945, a UE requestcomponent 950, or any combination thereof. Each of these components maycommunicate, directly or indirectly, with one another (e.g., via one ormore buses).

The communications manager 920 may support wireless communication at aUE in accordance with examples as disclosed herein. The UE machinelearning manager 930 may be configured as or otherwise support a meansfor receiving a machine learning model of one or more machine learningmodels, a set of parameters corresponding to the machine learning model,or a configuration corresponding to a neural network function of one ormore neural network functions, where the one or more machine learningmodels, the one or more neural network functions, or any combinationthereof may be associated with a machine learning MR that is included inor coupled with a base station. The UE activation component 935 may beconfigured as or otherwise support a means for receiving, from the basestation, an activation message for the machine learning model, theneural network function, or both.

In some examples, the UE request component 950 may be configured as orotherwise support a means for transmitting, to the base station, arequest message that includes an indication of the machine learningmodel, the neural network function, or both, where receiving the machinelearning model, the neural network function, or both is based on therequest message. In some examples, the UE request component 950 may beconfigured as or otherwise support a means for receiving, from the basestation, signaling indicating a first set of machine learning modelsincluded in a blacklist, a second set of machine learning modelsincluded in a whitelist, or both, where transmitting the request messageis based on the machine learning model being included in the whitelist,excluded from the blacklist, or both.

In some examples, each machine learning model of the one or more machinelearning models is associated with a respective scope corresponding to alocation, a network slice, a DNN, a PLMN, a UE type, a RRC state, acommunication service, a communication configuration, or any combinationthereof and transmitting the request message based on a trigger eventthat includes the UE having a condition that is within the respectivescope of the machine learning model.

In some examples, the request message includes an indication of thetrigger event. In some examples, to support transmitting the requestmessage, the UE request component 950 may be configured as or otherwisesupport a means for transmitting a UE assistance information messagethat includes the request message.

In some examples, to support transmitting the request message, the UErequest component 950 may be configured as or otherwise support a meansfor transmitting RRC signaling that includes the request message.

In some examples, the UE address manager 940 may be configured as orotherwise support a means for determining an address for the machinelearning model, the set of parameters, or the configuration based on anassociated identifier and an associated rule, where receiving themachine learning model, the set of parameters, or the configuration isbased on a download of the machine learning model, the set ofparameters, or the configuration from the machine learning MR based onthe address.

In some examples, the UE address manager 940 may be configured as orotherwise support a means for determining an address for a secondmachine learning model, a second set of parameters corresponding to thesecond machine learning model, or a second configuration correspondingto a second neural network function of the one or more neural networkfunctions based on an associated identifier and an associated rule. Insome examples, the UE upload component 945 may be configured as orotherwise support a means for initiating an upload of the second machinelearning model, the second set of parameters, or the secondconfiguration to the machine learning MR based on the address for thesecond machine learning model, the second set of parameters, or thesecond configuration.

In some examples, transmitting the request message includes transmittingthe request message to a CU-CP entity included in the base station. Insome examples, receiving the machine learning model, the set ofparameters, or the configuration includes receiving the machine learningmodel, the set of parameters, or the configuration from the CU-CPentity.

In some examples, the UE address manager 940 may be configured as orotherwise support a means for receiving an address for the machinelearning model, the set of parameters, or the configuration from a CU-XPentity included in the base station, where receiving the machinelearning model, the set of parameters, or the configuration is based ona download of the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based on the address.

In some examples, the UE address manager 940 may be configured as orotherwise support a means for receiving an address for a second machinelearning model, a second set of parameters corresponding to the secondmachine learning model, or a second configuration corresponding to asecond neural network function of the one or more neural networkfunctions from a CU-XP entity included in the base station. In someexamples, the UE upload component 945 may be configured as or otherwisesupport a means for initiating an upload of the second machine learningmodel, the second set of parameters, or the second configuration to themachine learning MR based on the address for the second machine learningmodel, the second set of parameters, or the second configuration.

FIG. 10 shows a diagram of a system 1000 including a device 1005 thatsupports configuring a UE for machine learning in accordance withaspects of the present disclosure. The device 1005 may be an example ofor include the components of a device 705, a device 805, or a UE 115 asdescribed herein. The device 1005 may communicate wirelessly with one ormore base stations 105, UEs 115, or any combination thereof. The device1005 may include components for bi-directional voice and datacommunications including components for transmitting and receivingcommunications, such as a communications manager 1020, an input/output(I/O) controller 1010, a transceiver 1015, an antenna 1025, a memory1030, code 1035, and a processor 1040. These components may be inelectronic communication or otherwise coupled (e.g., operatively,communicatively, functionally, electronically, electrically) via one ormore buses (e.g., a bus 1045).

The I/O controller 1010 may manage input and output signals for thedevice 1005. The I/O controller 1010 may also manage peripherals notintegrated into the device 1005. In some cases, the I/O controller 1010may represent a physical connection or port to an external peripheral.In some cases, the I/O controller 1010 may utilize an operating systemsuch as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®, UNIX®, LINUX®, oranother known operating system. Additionally or alternatively, the I/Ocontroller 1010 may represent or interact with a modem, a keyboard, amouse, a touchscreen, or a similar device. In some cases, the I/Ocontroller 1010 may be implemented as part of a processor, such as theprocessor 1040. In some cases, a user may interact with the device 1005via the I/O controller 1010 or via hardware components controlled by theI/O controller 1010.

In some cases, the device 1005 may include a single antenna 1025.However, in some other cases, the device 1005 may have more than oneantenna 1025, which may be capable of concurrently transmitting orreceiving multiple wireless transmissions. The transceiver 1015 maycommunicate bi-directionally, via the one or more antennas 1025, wired,or wireless links as described herein. For example, the transceiver 1015may represent a wireless transceiver and may communicatebi-directionally with another wireless transceiver. The transceiver 1015may also include a modem to modulate the packets, to provide themodulated packets to one or more antennas 1025 for transmission, and todemodulate packets received from the one or more antennas 1025. Thetransceiver 1015, or the transceiver 1015 and one or more antennas 1025,may be an example of a transmitter 715, a transmitter 815, a receiver710, a receiver 810, or any combination thereof or component thereof, asdescribed herein.

The memory 1030 may include random access memory (RAM) and read-onlymemory (ROM). The memory 1030 may store computer-readable,computer-executable code 1035 including instructions that, when executedby the processor 1040, cause the device 1005 to perform variousfunctions described herein. The code 1035 may be stored in anon-transitory computer-readable medium such as system memory or anothertype of memory. In some cases, the code 1035 may not be directlyexecutable by the processor 1040 but may cause a computer (e.g., whencompiled and executed) to perform functions described herein. In somecases, the memory 1030 may contain, among other things, a basic I/Osystem (BIOS) which may control basic hardware or software operationsuch as the interaction with peripheral components or devices.

The processor 1040 may include an intelligent hardware device (e.g., ageneral-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, anFPGA, a programmable logic device, a discrete gate or transistor logiccomponent, a discrete hardware component, or any combination thereof).In some cases, the processor 1040 may be configured to operate a memoryarray using a memory controller. In some other cases, a memorycontroller may be integrated into the processor 1040. The processor 1040may be configured to execute computer-readable instructions stored in amemory (e.g., the memory 1030) to cause the device 1005 to performvarious functions (e.g., functions or tasks supporting configuring a UEfor machine learning). For example, the device 1005 or a component ofthe device 1005 may include a processor 1040 and memory 1030 coupled tothe processor 1040, the processor 1040 and memory 1030 configured toperform various functions described herein.

The communications manager 1020 may support wireless communication at aUE in accordance with examples as disclosed herein. For example, thecommunications manager 1020 may be configured as or otherwise support ameans for receiving a machine learning model of one or more machinelearning models, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to a neural networkfunction of one or more neural network functions, where the one or moremachine learning models, the one or more neural network functions, orany combination thereof may be associated with a machine learning MRthat is included in or coupled with a base station. The communicationsmanager 1020 may be configured as or otherwise support a means forreceiving, from the base station, an activation message for the machinelearning model, the neural network function, or both.

By including or configuring the communications manager 1020 inaccordance with examples as described herein, the device 1005 maysupport techniques for reduced power consumption and improvedcoordination between devices.

In some examples, the communications manager 1020 may be configured toperform various operations (e.g., receiving, monitoring, transmitting)using or otherwise in cooperation with the transceiver 1015, the one ormore antennas 1025, or any combination thereof. Although thecommunications manager 1020 is illustrated as a separate component, insome examples, one or more functions described with reference to thecommunications manager 1020 may be supported by or performed by theprocessor 1040, the memory 1030, the code 1035, or any combinationthereof. For example, the code 1035 may include instructions executableby the processor 1040 to cause the device 1005 to perform variousaspects of configuring a UE for machine learning as described herein, orthe processor 1040 and the memory 1030 may be otherwise configured toperform or support such operations.

FIG. 11 shows a block diagram 1100 of a device 1105 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The device 1105 may be an example of aspects of abase station 105 as described herein. The device 1105 may include areceiver 1110, a transmitter 1115, and a communications manager 1120.The device 1105 may also include a processor. Each of these componentsmay be in communication with one another (e.g., via one or more buses).

The receiver 1110 may provide a means for receiving information such aspackets, user data, control information, or any combination thereofassociated with various information channels (e.g., control channels,data channels, information channels related to configuring a UE formachine learning). Information may be passed on to other components ofthe device 1105. The receiver 1110 may utilize a single antenna or a setof multiple antennas.

The transmitter 1115 may provide a means for transmitting signalsgenerated by other components of the device 1105. For example, thetransmitter 1115 may transmit information such as packets, user data,control information, or any combination thereof associated with variousinformation channels (e.g., control channels, data channels, informationchannels related to configuring a UE for machine learning). In someexamples, the transmitter 1115 may be co-located with a receiver 1110 ina transceiver module. The transmitter 1115 may utilize a single antennaor a set of multiple antennas.

The communications manager 1120, the receiver 1110, the transmitter1115, or various combinations thereof or various components thereof maybe examples of means for performing various aspects of configuring a UEfor machine learning as described herein. For example, thecommunications manager 1120, the receiver 1110, the transmitter 1115, orvarious combinations or components thereof may support a method forperforming one or more of the functions described herein.

In some examples, the communications manager 1120, the receiver 1110,the transmitter 1115, or various combinations or components thereof maybe implemented in hardware (e.g., in communications managementcircuitry). The hardware may include a processor, a DSP, an ASIC, anFPGA or other programmable logic device, a discrete gate or transistorlogic, discrete hardware components, or any combination thereofconfigured as or otherwise supporting a means for performing thefunctions described in the present disclosure. In some examples, aprocessor and memory coupled with the processor may be configured toperform one or more of the functions described herein (e.g., byexecuting, by the processor, instructions stored in the memory).

Additionally or alternatively, in some examples, the communicationsmanager 1120, the receiver 1110, the transmitter 1115, or variouscombinations or components thereof may be implemented in code (e.g., ascommunications management software or firmware) executed by a processor.If implemented in code executed by a processor, the functions of thecommunications manager 1120, the receiver 1110, the transmitter 1115, orvarious combinations or components thereof may be performed by ageneral-purpose processor, a DSP, a CPU, an ASIC, an FPGA, or anycombination of these or other programmable logic devices (e.g.,configured as or otherwise supporting a means for performing thefunctions described in the present disclosure).

In some examples, the communications manager 1120 may be configured toperform various operations (e.g., receiving, monitoring, transmitting)using or otherwise in cooperation with the receiver 1110, thetransmitter 1115, or both. For example, the communications manager 1120may receive information from the receiver 1110, send information to thetransmitter 1115, or be integrated in combination with the receiver1110, the transmitter 1115, or both to receive information, transmitinformation, or perform various other operations as described herein.

The communications manager 1120 may support wireless communication at abase station in accordance with examples as disclosed herein. Forexample, the communications manager 1120 may be configured as orotherwise support a means for transmitting, to the UE, a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station. The communications manager 1120 may be configuredas or otherwise support a means for transmitting, to the UE, anactivation message for the machine learning model, the neural networkfunction, or both.

By including or configuring the communications manager 1120 inaccordance with examples as described herein, the device 1105 (e.g., aprocessor controlling or otherwise coupled to the receiver 1110, thetransmitter 1115, the communications manager 1120, or a combinationthereof) may support techniques for reduced processing and reduced powerconsumption.

FIG. 12 shows a block diagram 1200 of a device 1205 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The device 1205 may be an example of aspects of adevice 1105 or a base station 105 as described herein. The device 1205may include a receiver 1210, a transmitter 1215, and a communicationsmanager 1220. The device 1205 may also include a processor. Each ofthese components may be in communication with one another (e.g., via oneor more buses).

The receiver 1210 may provide a means for receiving information such aspackets, user data, control information, or any combination thereofassociated with various information channels (e.g., control channels,data channels, information channels related to configuring a UE formachine learning). Information may be passed on to other components ofthe device 1205. The receiver 1210 may utilize a single antenna or a setof multiple antennas.

The transmitter 1215 may provide a means for transmitting signalsgenerated by other components of the device 1205. For example, thetransmitter 1215 may transmit information such as packets, user data,control information, or any combination thereof associated with variousinformation channels (e.g., control channels, data channels, informationchannels related to configuring a UE for machine learning). In someexamples, the transmitter 1215 may be co-located with a receiver 1210 ina transceiver module. The transmitter 1215 may utilize a single antennaor a set of multiple antennas.

The device 1205, or various components thereof, may be an example ofmeans for performing various aspects of configuring a UE for machinelearning as described herein. For example, the communications manager1220 may include a machine learning manager 1230, an activationcomponent 1235, or any combination thereof. The communications manager1220 may be an example of aspects of a communications manager 1120 asdescribed herein. In some examples, the communications manager 1220, orvarious components thereof, may be configured to perform variousoperations (e.g., receiving, monitoring, transmitting) using orotherwise in cooperation with the receiver 1210, the transmitter 1215,or both. For example, the communications manager 1220 may receiveinformation from the receiver 1210, send information to the transmitter1215, or be integrated in combination with the receiver 1210, thetransmitter 1215, or both to receive information, transmit information,or perform various other operations as described herein.

The communications manager 1220 may support wireless communication at abase station in accordance with examples as disclosed herein. Themachine learning manager 1230 may be configured as or otherwise supporta means for transmitting, to the UE, a machine learning model of one ormore machine learning models, a set of parameters corresponding to themachine learning model, or a configuration corresponding to a neuralnetwork function of one or more neural network functions, where the oneor more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with a base station. Theactivation component 1235 may be configured as or otherwise support ameans for transmitting, to the UE, an activation message for the machinelearning model, the neural network function, or both.

FIG. 13 shows a block diagram 1300 of a communications manager 1320 thatsupports configuring a UE for machine learning in accordance withaspects of the present disclosure. The communications manager 1320 maybe an example of aspects of a communications manager 1120, acommunications manager 1220, or both, as described herein. Thecommunications manager 1320, or various components thereof, may be anexample of means for performing various aspects of configuring a UE formachine learning as described herein. For example, the communicationsmanager 1320 may include a machine learning manager 1330, an activationcomponent 1335, an address component 1340, a download component 1345, anupload component 1350, an identifier component 1355, a request component1360, or any combination thereof. Each of these components maycommunicate, directly or indirectly, with one another (e.g., via one ormore buses).

The communications manager 1320 may support wireless communication at abase station in accordance with examples as disclosed herein. Themachine learning manager 1330 may be configured as or otherwise supporta means for transmitting, to the UE, a machine learning model of one ormore machine learning models, a set of parameters corresponding to themachine learning model, or a configuration corresponding to a neuralnetwork function of one or more neural network functions, where the oneor more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with the base station. Theactivation component 1335 may be configured as or otherwise support ameans for transmitting, to the UE, an activation message for the machinelearning model, the neural network function, or both.

In some examples, the request component 1360 may be configured as orotherwise support a means for receiving, from the UE, a request messagethat includes an indication of the machine learning model, the neuralnetwork function, or both, where transmitting the machine learningmodel, the neural network function, or both based on the requestmessage. In some examples, the request component 1360 may be configuredas or otherwise support a means for transmitting, to the UE, signalingindicating a first set of machine learning models included in ablacklist, a second set of machine learning models included in awhitelist, or both, where the machine learning model is included in thewhitelist, excluded from the blacklist, or both. In some examples, therequest message includes an indication of the trigger event.

In some examples, each machine learning model of the one or more machinelearning models is associated with a respective scope corresponding to alocation, a network slice, a DNN, a PLMN, a UE type, a RRC state, acommunication service, a communication configuration, or any combinationthereof and receiving the request message based on a trigger event thatincludes the UE having a condition that is within the respective scopeof the machine learning model.

In some examples, to support receiving the request message, the requestcomponent 1360 may be configured as or otherwise support a means forreceiving a UE assistance information message that includes the requestmessage.

In some examples, to support receiving the request message, the requestcomponent 1360 may be configured as or otherwise support a means forreceiving RRC signaling that includes the request message.

In some examples, the address component 1340 may be configured as orotherwise support a means for receiving, from the UE, an address for themachine learning model, the set of parameters, or the configuration. Insome examples, the download component 1345 may be configured as orotherwise support a means for downloading, for the UE, the machinelearning model, the set of parameters, or the configuration from themachine learning MR based on the address.

In some examples, the address component 1340 may be configured as orotherwise support a means for receiving, from the UE, an address for asecond machine learning model, a second set of parameters correspondingto the second machine learning model, or a second configurationcorresponding to a second neural network function of the one or moreneural network functions. In some examples, the upload component 1350may be configured as or otherwise support a means for uploading thesecond machine learning model, the second set of parameters, or thesecond configuration to the machine learning MR.

In some examples, the request component 1360 may be configured as orotherwise support a means for receiving the request message at a CU-CPentity included in the base station. In some examples, the requestcomponent 1360 may be configured as or otherwise support a means forforwarding the request message from the CU-CP entity to a CU-XP entityincluded in the base station. In some examples, the download component1345 may be configured as or otherwise support a means for downloading,to the CU-CP entity, the machine learning model, the set of parameters,or the configuration from the machine learning MR based on the requestmessage, where transmitting the machine learning model, the set ofparameters, or the configuration to the UE is based on the downloading.

In some examples, the identifier component 1355 may be configured as orotherwise support a means for receiving, from the UE at a CU-XP entityincluded in the base station, an identifier associated with the machinelearning model, the set of parameters, or the configuration. In someexamples, the address component 1340 may be configured as or otherwisesupport a means for determining an address for the machine learningmodel, the set of parameters, or the configuration based at least inpart on the identifier. In some examples, the download component 1345may be configured as or otherwise support a means for downloading, forthe UE, the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based on the address, wheretransmitting the machine learning model, the set of parameters, or theconfiguration to the UE is based on the downloading.

In some examples, the identifier component 1355 may be configured as orotherwise support a means for receiving, from the UE at a CU-XP entityincluded in the base station, an identifier associated with a secondmachine learning model, a second set of parameters corresponding to thesecond machine learning model, or a second configuration correspondingto a second neural network function of the one or more neural networkfunctions. In some examples, the address component 1340 may beconfigured as or otherwise support a means for determining an addressfor the second machine learning model, the second set of parameters, orthe second configuration based at least in part on the identifier. Insome examples, the upload component 1350 may be configured as orotherwise support a means for uploading, to the machine learning MR, thesecond machine learning model, the second set of parameters, or thesecond configuration based on the address.

FIG. 14 shows a diagram of a system 1400 including a device 1405 thatsupports configuring a UE for machine learning in accordance withaspects of the present disclosure. The device 1405 may be an example ofor include the components of a device 1105, a device 1205, or a basestation 105 as described herein. The device 1405 may communicatewirelessly with one or more base stations 105, UEs 115, or anycombination thereof. The device 1405 may include components forbi-directional voice and data communications including components fortransmitting and receiving communications, such as a communicationsmanager 1420, a network communications manager 1410, a transceiver 1415,an antenna 1425, a memory 1430, code 1435, a processor 1440, and aninter-station communications manager 1445. These components may be inelectronic communication or otherwise coupled (e.g., operatively,communicatively, functionally, electronically, electrically) via one ormore buses (e.g., a bus 1450).

The network communications manager 1410 may manage communications with acore network 130 (e.g., via one or more wired backhaul links). Forexample, the network communications manager 1410 may manage the transferof data communications for client devices, such as one or more UEs 115.

In some cases, the device 1405 may include a single antenna 1425.However, in some other cases the device 1405 may have more than oneantenna 1425, which may be capable of concurrently transmitting orreceiving multiple wireless transmissions. The transceiver 1415 maycommunicate bi-directionally, via the one or more antennas 1425, wired,or wireless links as described herein. For example, the transceiver 1415may represent a wireless transceiver and may communicatebi-directionally with another wireless transceiver. The transceiver 1415may also include a modem to modulate the packets, to provide themodulated packets to one or more antennas 1425 for transmission, and todemodulate packets received from the one or more antennas 1425. Thetransceiver 1415, or the transceiver 1415 and one or more antennas 1425,may be an example of a transmitter 1115, a transmitter 1215, a receiver1110, a receiver 1210, or any combination thereof or component thereof,as described herein.

The memory 1430 may include RAM and ROM. The memory 1430 may storecomputer-readable, computer-executable code 1435 including instructionsthat, when executed by the processor 1440, cause the device 1405 toperform various functions described herein. The code 1435 may be storedin a non-transitory computer-readable medium such as system memory oranother type of memory. In some cases, the code 1435 may not be directlyexecutable by the processor 1440 but may cause a computer (e.g., whencompiled and executed) to perform functions described herein. In somecases, the memory 1430 may contain, among other things, a BIOS which maycontrol basic hardware or software operation such as the interactionwith peripheral components or devices.

The processor 1440 may include an intelligent hardware device (e.g., ageneral-purpose processor, a DSP, a CPU, a microcontroller, an ASIC, anFPGA, a programmable logic device, a discrete gate or transistor logiccomponent, a discrete hardware component, or any combination thereof).In some cases, the processor 1440 may be configured to operate a memoryarray using a memory controller. In some other cases, a memorycontroller may be integrated into the processor 1440. The processor 1440may be configured to execute computer-readable instructions stored in amemory (e.g., the memory 1430) to cause the device 1405 to performvarious functions (e.g., functions or tasks supporting configuring a UEfor machine learning). For example, the device 1405 or a component ofthe device 1405 may include a processor 1440 and memory 1430 coupled tothe processor 1440, the processor 1440 and memory 1430 configured toperform various functions described herein.

The inter-station communications manager 1445 may manage communicationswith other base stations 105, and may include a controller or schedulerfor controlling communications with UEs 115 in cooperation with otherbase stations 105. For example, the inter-station communications manager1445 may coordinate scheduling for transmissions to UEs 115 for variousinterference mitigation techniques such as beamforming or jointtransmission. In some examples, the inter-station communications manager1445 may provide an X2 interface within an LTE/LTE-A wirelesscommunications network technology to provide communication between basestations 105.

The communications manager 1420 may support wireless communication at abase station in accordance with examples as disclosed herein. Forexample, the communications manager 1420 may be configured as orotherwise support a means for transmitting, to the UE, a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station. The communications manager 1420 may be configuredas or otherwise support a means for transmitting, to the UE, anactivation message for the machine learning model, the neural networkfunction, or both.

By including or configuring the communications manager 1420 inaccordance with examples as described herein, the device 1405 maysupport techniques for reduced power consumption and improvedcoordination between devices.

In some examples, the communications manager 1420 may be configured toperform various operations (e.g., receiving, monitoring, transmitting)using or otherwise in cooperation with the transceiver 1415, the one ormore antennas 1425, or any combination thereof. Although thecommunications manager 1420 is illustrated as a separate component, insome examples, one or more functions described with reference to thecommunications manager 1420 may be supported by or performed by theprocessor 1440, the memory 1430, the code 1435, or any combinationthereof. For example, the code 1435 may include instructions executableby the processor 1440 to cause the device 1405 to perform variousaspects of configuring a UE for machine learning as described herein, orthe processor 1440 and the memory 1430 may be otherwise configured toperform or support such operations.

FIG. 15 shows a flowchart illustrating a method 1500 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The operations of the method 1500 may be implementedby a UE or its components as described herein. For example, theoperations of the method 1500 may be performed by a UE 115 as describedwith reference to FIGS. 1 through 10 . In some examples, a UE mayexecute a set of instructions to control the functional elements of theUE to perform the described functions. Additionally or alternatively,the UE may perform aspects of the described functions usingspecial-purpose hardware.

At 1505, the method may include receiving a machine learning model ofone or more machine learning models, a set of parameters correspondingto the machine learning model, or a configuration corresponding to aneural network function of one or more neural network functions, wherethe one or more machine learning models, the one or more neural networkfunctions, or any combination thereof may be associated with a machinelearning MR that is included in or coupled with a base station. Theoperations of 1505 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1505may be performed by a UE machine learning manager 930 as described withreference to FIG. 9 .

At 1510, the method may include receiving, from the base station, anactivation message for the machine learning model, the neural networkfunction, or both. The operations of 1510 may be performed in accordancewith examples as disclosed herein. In some examples, aspects of theoperations of 1510 may be performed by a UE activation component 935 asdescribed with reference to FIG. 9 .

FIG. 16 shows a flowchart illustrating a method 1600 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The operations of the method 1600 may be implementedby a UE or its components as described herein. For example, theoperations of the method 1600 may be performed by a UE 115 as describedwith reference to FIGS. 1 through 10 . In some examples, a UE mayexecute a set of instructions to control the functional elements of theUE to perform the described functions. Additionally or alternatively,the UE may perform aspects of the described functions usingspecial-purpose hardware.

At 1605, the method may include transmitting, to a base station, arequest message that comprises an indication of a machine learning modelof one or more machine learning models, a neural network function of oneor more neural network functions, or both, wherein the one or moremachine learning models, the one or more neural network functions, orany combination thereof are associated with a machine learning modelrepository that is included in or coupled with a base station. Theoperations of 1605 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1605may be performed by a UE request component 950 as described withreference to FIG. 9 .

At 1610, the method may include receiving, from the base station, themachine learning model, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to the neural networkfunction based at least in part on transmitting the request message. Theoperations of 1610 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1610may be performed by a UE machine learning manager 930 as described withreference to FIG. 9 .

At 1615, the method may include receiving, from the base station, anactivation message for the machine learning model, the neural networkfunction, or both. The operations of 1615 may be performed in accordancewith examples as disclosed herein. In some examples, aspects of theoperations of 1615 may be performed by a UE activation component 935 asdescribed with reference to FIG. 9 .

FIG. 17 shows a flowchart illustrating a method 1700 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The operations of the method 1700 may be implementedby a base station or its components as described herein. For example,the operations of the method 1700 may be performed by a base station 105as described with reference to FIGS. 1 through 6 and 11 through 14 . Insome examples, a base station may execute a set of instructions tocontrol the functional elements of the base station to perform thedescribed functions. Additionally or alternatively, the base station mayperform aspects of the described functions using special-purposehardware.

At 1705, the method may include transmitting, to the UE, a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, where the one or more machine learning models,the one or more neural network functions, or any combination thereof maybe associated with a machine learning MR that is included in or coupledwith the base station. The operations of 1705 may be performed inaccordance with examples as disclosed herein. In some examples, aspectsof the operations of 1705 may be performed by a machine learning manager1330 as described with reference to FIG. 13 .

At 1710, the method may include transmitting, to the UE, an activationmessage for the machine learning model, the neural network function, orboth. The operations of 1710 may be performed in accordance withexamples as disclosed herein. In some examples, aspects of theoperations of 1710 may be performed by an activation component 1335 asdescribed with reference to FIG. 13 .

FIG. 18 shows a flowchart illustrating a method 1800 that supportsconfiguring a UE for machine learning in accordance with aspects of thepresent disclosure. The operations of the method 1800 may be implementedby a base station or its components as described herein. For example,the operations of the method 1800 may be performed by a base station 105as described with reference to FIGS. 1 through 6 and 11 through 14 . Insome examples, a base station may execute a set of instructions tocontrol the functional elements of the base station to perform thedescribed functions. Additionally or alternatively, the base station mayperform aspects of the described functions using special-purposehardware.

At 1805, the method may include receiving, from the UE, a requestmessage that comprises an indication of a machine learning model of oneor more machine learning models, a neural network function of one ormore neural network functions, or both, wherein the one or more machinelearning models, the one or more neural network functions, or anycombination thereof are associated with a machine learning modelrepository that is included in or coupled with a base station. Theoperations of 1805 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1805may be performed by a request component 1360 as described with referenceto FIG. 13 .

At 1810, the method may include transmitting, to the UE, the machinelearning model, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to the neural networkfunction based at least in part on receiving the request message. Theoperations of 1810 may be performed in accordance with examples asdisclosed herein. In some examples, aspects of the operations of 1810may be performed by a machine learning manager 1330 as described withreference to FIG. 13 .

At 1815, the method may include transmitting, to the UE, an activationmessage for the machine learning model, the neural network function, orboth. The operations of 1815 may be performed in accordance withexamples as disclosed herein. In some examples, aspects of theoperations of 1815 may be performed by an activation component 1335 asdescribed with reference to FIG. 13 .

The following provides an overview of aspects of the present disclosure:

Aspect 1: A method for wireless communication at a UE, comprising:receiving a machine learning model of one or more machine learningmodels, a set of parameters corresponding to the machine learning model,or a configuration corresponding to a neural network function of one ormore neural network functions, wherein the one or more machine learningmodels, the one or more neural network functions, or any combinationthereof are associated with a machine learning MR that is included in orcoupled with a base station; and receiving, from the base station, anactivation message for the machine learning model, the neural networkfunction, or both.

Aspect 2: The method of aspect 1, further comprising: transmitting, tothe base station, a request message that comprises an indication of themachine learning model, the neural network function, or both, whereinreceiving the machine learning model, the neural network function, orboth is based at least in part on the request message.

Aspect 3: The method of aspect 2, further comprising: receiving, fromthe base station, signaling indicating a first set of machine learningmodels included in a blacklist, a second set of machine learning modelsincluded in a whitelist, or both, wherein transmitting the requestmessage is based at least in part on the machine learning model beingincluded in the whitelist, excluded from the blacklist, or both.

Aspect 4: The method of any of aspects 2 through 3, wherein each machinelearning model of the one or more machine learning models is associatedwith a respective scope corresponding to a location, a network slice, aDNN, a PLMN, a UE type, a RRC state, a communication service, acommunication configuration, or any combination thereof and transmittingthe request message is based at least in part on a trigger event thatcomprises the UE having a condition that is within the respective scopeof the machine learning model.

Aspect 5: The method of aspect 4, wherein the request message comprisesan indication of the trigger event.

Aspect 6: The method of any of aspects 2 through 5, wherein transmittingthe request message comprises: transmitting a UE assistance informationmessage that comprises the request message.

Aspect 7: The method of any of aspects 2 through 6, wherein transmittingthe request message comprises: transmitting RRC signaling that comprisesthe request message.

Aspect 8: The method of any of aspects 2 through 7, wherein transmittingthe request message comprises transmitting the request message to aCU-CP entity included in the base station; and receiving the machinelearning model, the set of parameters, or the configuration comprisesreceiving the machine learning model, the set of parameters, or theconfiguration from the CU-CP entity.

Aspect 9: The method of any of aspects 1 through 7, further comprising:determining an address for the machine learning model, the set ofparameters, or the configuration based at least in part on an associatedID and an associated rule, wherein receiving the machine learning model,the set of parameters, or the configuration is based at least in part ona download of the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based at least in part on theaddress.

Aspect 10: The method of any of aspects 1 through 7, further comprising:determining an address for a second machine learning model, a second setof parameters corresponding to the second machine learning model, or asecond configuration corresponding to a second neural network functionof the one or more neural network functions based at least in part on anassociated ID and an associated rule; and initiating an upload of thesecond machine learning model, the second set of parameters, or thesecond configuration to the machine learning MR based at least in parton the address for the second machine learning model, the second set ofparameters, or the second configuration.

Aspect 11: The method of any of aspects 1 through 7, further comprising:receiving an address for the machine learning model, the set ofparameters, or the configuration from a CU-XP entity included in thebase station, wherein receiving the machine learning model, the set ofparameters, or the configuration is based at least in part on a downloadof the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based at least in part on theaddress.

Aspect 12: The method of any of aspects 1 through 7, further comprising:receiving an address for a second machine learning model, a second setof parameters corresponding to the second machine learning model, or asecond configuration corresponding to a second neural network functionof the one or more neural network functions from a CU-XP entity includedin the base station; and initiating an upload of the second machinelearning model, the second set of parameters, or the secondconfiguration to the machine learning MR based at least in part on theaddress for the second machine learning model, the second set ofparameters, or the second configuration.

Aspect 13: A method for wireless communication at a base station,comprising: transmitting, to the UE, a machine learning model of one ormore machine learning models, a set of parameters corresponding to themachine learning model, or a configuration corresponding to a neuralnetwork function of one or more neural network functions, wherein theone or more machine learning models, the one or more neural networkfunctions, or any combination thereof are associated with a machinelearning MR that is included in or coupled with the base station; andtransmitting, to the UE, an activation message for the machine learningmodel, the neural network function, or both.

Aspect 14: The method of aspect 13, further comprising: receiving, fromthe UE, a request message that includes an indication of the machinelearning model, the neural network function, or both, whereintransmitting the machine learning model, the neural network function, orboth is based at least in part on the request message.

Aspect 15: The method of aspect 14, further comprising: transmitting, tothe UE, signaling indicating a first set of machine learning modelsincluded in a blacklist, a second set of machine learning modelsincluded in a whitelist, or both, wherein the machine learning model isincluded in the whitelist, excluded from the blacklist, or both.

Aspect 16: The method of any of aspects 14 through 15, wherein eachmachine learning model of the one or more machine learning models isassociated with a respective scope corresponding to a location, anetwork slice, a DNN, a PLMN, a UE type, a RRC state, a communicationservice, a communication configuration, or any combination thereof andreceiving the request message is based at least in part on a triggerevent that comprises the UE having a condition that is within therespective scope of the machine learning model.

Aspect 17: The method of aspect 16, wherein the request messagecomprises an indication of the trigger event.

Aspect 18: The method of any of aspects 14 through 17, wherein receivingthe request message comprises: receiving a UE assistance informationmessage that comprises the request message.

Aspect 19: The method of any of aspects 14 through 18, wherein receivingthe request message comprises: receiving RRC signaling that comprisesthe request message.

Aspect 20: The method of any of aspects 14 through 19, furthercomprising: receiving the request message at a CU-CP entity included inthe base station; forwarding the request message from the CU-CP entityto a CU-XP entity included in the base station; and downloading, to theCU-CP entity, the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based at least in part on therequest message, wherein transmitting the machine learning model, theset of parameters, or the configuration to the UE is based at least inpart on the downloading.

Aspect 21: The method of any of aspects 13 through 19, furthercomprising: receiving, from the UE, an address for the machine learningmodel, the set of parameters, or the configuration; and downloading, forthe UE, the machine learning model, the set of parameters, or theconfiguration from the machine learning MR based at least in part on theaddress.

Aspect 22: The method of any of aspects 13 through 19, furthercomprising: receiving, from the UE, an address for a second machinelearning model, a second set of parameters corresponding to the secondmachine learning model, or a second configuration corresponding to asecond neural network function of the one or more neural networkfunctions; and uploading the second machine learning model, the secondset of parameters, or the second configuration to the machine learningMR.

Aspect 23: The method of any of aspects 13 through 19, furthercomprising: receiving, from the UE at a CU-XP entity included in thebase station, an ID associated with the machine learning model, the setof parameters, or the configuration; determining an address for themachine learning model, the set of parameters, or the configurationbased at least in part on the ID; and downloading, for the UE, themachine learning model, the set of parameters, or the configuration fromthe machine learning MR based at least in part on the address, whereintransmitting the machine learning model, the set of parameters, or theconfiguration to the UE is based at least in part on the downloading.

Aspect 24: The method of any of aspects 13 through 19, furthercomprising: receiving, from the UE at a CU-XP entity included in thebase station, an ID associated with a second machine learning model, asecond set of parameters corresponding to the second machine learningmodel, or a second configuration corresponding to a second neuralnetwork function of the one or more neural network functions;determining an address for the second machine learning model, the secondset of parameters, or the second configuration based at least in part onthe ID; and uploading, to the machine learning MR, the second machinelearning model, the second set of parameters, or the secondconfiguration based at least in part on the address.

Aspect 25: An apparatus for wireless communication at a UE, comprising aprocessor; memory coupled with the processor; and instructions stored inthe memory and executable by the processor to cause the apparatus toperform a method of any of aspects 1 through 12.

Aspect 26: An apparatus for wireless communication at a UE, comprisingat least one means for performing a method of any of aspects 1 through12.

Aspect 27: A non-transitory computer-readable medium storing code forwireless communication at a UE, the code comprising instructionsexecutable by a processor to perform a method of any of aspects 1through 12.

Aspect 28: An apparatus for wireless communication at a base station,comprising a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to perform a method of any of aspects 13 through 24.

Aspect 29: An apparatus for wireless communication at a base station,comprising at least one means for performing a method of any of aspects13 through 24.

Aspect 30: A non-transitory computer-readable medium storing code forwireless communication at a base station, the code comprisinginstructions executable by a processor to perform a method of any ofaspects 13 through 24.

It should be noted that the methods described herein describe possibleimplementations, and that the operations and the steps may be rearrangedor otherwise modified and that other implementations are possible.Further, aspects from two or more of the methods may be combined.

Although aspects of an LTE, LTE-A, LTE-A Pro, or NR system may bedescribed for purposes of example, and LTE, LTE-A, LTE-A Pro, or NRterminology may be used in much of the description, the techniquesdescribed herein are applicable beyond LTE, LTE-A, LTE-A Pro, or NRnetworks. For example, the described techniques may be applicable tovarious other wireless communications systems such as Ultra MobileBroadband (UMB), Institute of Electrical and Electronics Engineers(IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDM, aswell as other systems and radio technologies not explicitly mentionedherein.

Information and signals described herein may be represented using any ofa variety of different technologies and techniques. For example, data,instructions, commands, information, signals, bits, symbols, and chipsthat may be referenced throughout the description may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof.

The various illustrative blocks and components described in connectionwith the disclosure herein may be implemented or performed with ageneral-purpose processor, a DSP, an ASIC, a CPU, an FPGA or otherprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A general-purpose processor may be amicroprocessor, but in the alternative, the processor may be anyprocessor, controller, microcontroller, or state machine. A processormay also be implemented as a combination of computing devices (e.g., acombination of a DSP and a microprocessor, multiple microprocessors, oneor more microprocessors in conjunction with a DSP core, or any othersuch configuration).

The functions described herein may be implemented in hardware, softwareexecuted by a processor, firmware, or any combination thereof. Ifimplemented in software executed by a processor, the functions may bestored on or transmitted over as one or more instructions or code on acomputer-readable medium. Other examples and implementations are withinthe scope of the disclosure and appended claims. For example, due to thenature of software, functions described herein may be implemented usingsoftware executed by a processor, hardware, firmware, hardwiring, orcombinations of any of these. Features implementing functions may alsobe physically located at various positions, including being distributedsuch that portions of functions are implemented at different physicallocations.

Computer-readable media includes both non-transitory computer storagemedia and communication media including any medium that facilitatestransfer of a computer program from one place to another. Anon-transitory storage medium may be any available medium that may beaccessed by a general-purpose or special-purpose computer. By way ofexample, and not limitation, non-transitory computer-readable media mayinclude RAM, ROM, electrically erasable programmable ROM (EEPROM), flashmemory, compact disk (CD) ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any othernon-transitory medium that may be used to carry or store desired programcode means in the form of instructions or data structures and that maybe accessed by a general-purpose or special-purpose computer, or ageneral-purpose or special-purpose processor. Also, any connection isproperly termed a computer-readable medium. For example, if the softwareis transmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio, and microwave areincluded in the definition of computer-readable medium. Disk and disc,as used herein, include CD, laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above are also included within the scope ofcomputer-readable media.

As used herein, including in the claims, “or” as used in a list of items(e.g., a list of items prefaced by a phrase such as “at least one of” or“one or more of”) indicates an inclusive list such that, for example, alist of at least one of A, B, or C means A or B or C or AB or AC or BCor ABC (i.e., A and B and C). Also, as used herein, the phrase “basedon” shall not be construed as a reference to a closed set of conditions.For example, an example step that is described as “based on condition A”may be based on both a condition A and a condition B without departingfrom the scope of the present disclosure. In other words, as usedherein, the phrase “based on” shall be construed in the same manner asthe phrase “based at least in part on.” Also, as used herein, the phrase“a set” shall be construed as including the possibility of a set withone member. That is, the phrase “a set” shell be construed in the samemanner as “one or more.”

The term “determine” or “determining” encompasses a wide variety ofactions and, therefore, “determining” can include calculating,computing, processing, deriving, investigating, looking up (such as vialooking up in a table, a database or another data structure),ascertaining and the like. Also, “determining” can include receiving(such as receiving information), accessing (such as accessing data in amemory) and the like. Also, “determining” can include resolving,selecting, choosing, establishing and other such similar actions.

In the appended figures, similar components or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If just the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label, or othersubsequent reference label.

The description set forth herein, in connection with the appendeddrawings, describes example configurations and does not represent allthe examples that may be implemented or that are within the scope of theclaims. The term “example” used herein means “serving as an example,instance, or illustration,” and not “preferred” or “advantageous overother examples.” The detailed description includes specific details forthe purpose of providing an understanding of the described techniques.These techniques, however, may be practiced without these specificdetails. In some instances, known structures and devices are shown inblock diagram form in order to avoid obscuring the concepts of thedescribed examples.

The description herein is provided to enable a person having ordinaryskill in the art to make or use the disclosure. Various modifications tothe disclosure will be apparent to a person having ordinary skill in theart, and the generic principles defined herein may be applied to othervariations without departing from the scope of the disclosure. Thus, thedisclosure is not limited to the examples and designs described hereinbut is to be accorded the broadest scope consistent with the principlesand novel features disclosed herein.

What is claimed is:
 1. An apparatus for wireless communication at a userequipment (UE), comprising: a processor; memory coupled with theprocessor; and instructions stored in the memory and executable by theprocessor to cause the apparatus to: receive a machine learning model ofone or more machine learning models, a set of parameters correspondingto the machine learning model, or a configuration corresponding to aneural network function of one or more neural network functions, whereinthe one or more machine learning models, the one or more neural networkfunctions, or any combination thereof are associated with a machinelearning model repository that is included in or coupled with a basestation; and receive, from the base station, an activation message forthe machine learning model, the neural network function, or both.
 2. Theapparatus of claim 1, wherein the instructions are further executable bythe processor to cause the apparatus to: transmit, to the base station,a request message that comprises an indication of the machine learningmodel, the neural network function, or both, wherein receiving themachine learning model, the neural network function, or both is based atleast in part on the request message.
 3. The apparatus of claim 2,wherein the instructions are further executable by the processor tocause the apparatus to: receive, from the base station, signalingindicating a first set of machine learning models included in ablacklist, a second set of machine learning models included in awhitelist, or both, wherein transmitting the request message is based atleast in part on the machine learning model being included in thewhitelist, excluded from the blacklist, or both.
 4. The apparatus ofclaim 2, wherein: each machine learning model of the one or more machinelearning models is associated with a respective scope corresponding to alocation, a network slice, a deep neural network, a public land mobilenetwork, a UE type, a radio resource control state, a communicationservice, a communication configuration, or any combination thereof; andthe instructions are executable by the processor to cause the apparatusto transmit the request message based at least in part on a triggerevent that comprises the UE having a condition that is within therespective scope of the machine learning model.
 5. The apparatus ofclaim 4, wherein the request message comprises an indication of thetrigger event.
 6. The apparatus of claim 2, wherein, to transmit therequest message, the instructions are executable by the processor tocause the apparatus to: transmit a UE assistance information messagethat comprises the request message.
 7. The apparatus of claim 2,wherein, to transmit the request message, the instructions areexecutable by the processor to cause the apparatus to: transmit radioresource control signaling that comprises the request message.
 8. Theapparatus of claim 2, wherein: to transmit the request message, theinstructions are executable by the processor to cause the apparatus totransmit the request message to a central unit-control plane entityincluded in the base station; and to receive the machine learning model,the set of parameters, or the configuration, the instructions areexecutable by the processor to cause the apparatus to receive themachine learning model, the set of parameters, or the configuration fromthe central unit-control plane entity.
 9. The apparatus of claim 1,wherein the instructions are further executable by the processor tocause the apparatus to: determine an address for the machine learningmodel, the set of parameters, or the configuration based at least inpart on an associated identifier and an associated rule, wherein theinstructions are executable by the processor to cause the apparatus toreceive the machine learning model, the set of parameters, or theconfiguration based at least in part on a download of the machinelearning model, the set of parameters, or the configuration from themachine learning model repository based at least in part on the address.10. The apparatus of claim 1, wherein the instructions are furtherexecutable by the processor to cause the apparatus to: determine anaddress for a second machine learning model, a second set of parameterscorresponding to the second machine learning model, or a secondconfiguration corresponding to a second neural network function of theone or more neural network functions based at least in part on anassociated identifier and an associated rule; and initiate an upload ofthe second machine learning model, the second set of parameters, or thesecond configuration to the machine learning model repository based atleast in part on the address for the second machine learning model, thesecond set of parameters, or the second configuration.
 11. The apparatusof claim 1, wherein the instructions are further executable by theprocessor to cause the apparatus to: receive an address for the machinelearning model, the set of parameters, or the configuration from acentral unit-machine learning plane entity included in the base station,wherein the instructions are executable by the processor to cause theapparatus to receive the machine learning model, the set of parameters,or the configuration based at least in part on a download of the machinelearning model, the set of parameters, or the configuration from themachine learning model repository based at least in part on the address.12. The apparatus of claim 1, wherein the instructions are furtherexecutable by the processor to cause the apparatus to: receive anaddress for a second machine learning model, a second set of parameterscorresponding to the second machine learning model, or a secondconfiguration corresponding to a second neural network function of theone or more neural network functions from a central unit-machinelearning plane entity included in the base station; and initiate anupload of the second machine learning model, the second set ofparameters, or the second configuration to the machine learning modelrepository based at least in part on the address for the second machinelearning model, the second set of parameters, or the secondconfiguration.
 13. An apparatus for wireless communication at a basestation, comprising: a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to: transmit, to a user equipment (UE), a machinelearning model of one or more machine learning models, a set ofparameters corresponding to the machine learning model, or aconfiguration corresponding to a neural network function of one or moreneural network functions, wherein the one or more machine learningmodels, the one or more neural network functions, or any combinationthereof are associated with a machine learning model repository that isincluded in or coupled with the base station; and transmit, to the UE,an activation message for the machine learning model, the neural networkfunction, or both.
 14. The apparatus of claim 13, wherein theinstructions are further executable by the processor to cause theapparatus to: receive, from the UE, a request message that includes anindication of the machine learning model, the neural network function,or both, wherein transmitting the machine learning model, the neuralnetwork function, or both is based at least in part on the requestmessage.
 15. The apparatus of claim 14, wherein the instructions arefurther executable by the processor to cause the apparatus to: transmit,to the UE, signaling indicating a first set of machine learning modelsincluded in a blacklist, a second set of machine learning modelsincluded in a whitelist, or both, wherein receiving the request messageis based at least in part on the machine learning model being includedin the whitelist, excluded from the blacklist, or both.
 16. Theapparatus of claim 14, wherein: each machine learning model of the oneor more machine learning models is associated with a respective scopecorresponding to a location, a network slice, a deep neural network, apublic land mobile network, a UE type, a radio resource control state, acommunication service, a communication configuration, or any combinationthereof; and the instructions are executable by the processor to causesthe apparatus to receive the request message based at least in part on atrigger event that comprises the UE having a condition that is withinthe respective scope of the machine learning model.
 17. The apparatus ofclaim 16, wherein the request message comprises an indication of thetrigger event.
 18. The apparatus of claim 14, wherein, to receive therequest message, the instructions are executable by the processor tocause the apparatus to: receive a UE assistance information message thatcomprises the request message.
 19. The apparatus of claim 14, wherein,to receive the request message, the instructions are executable by theprocessor to cause the apparatus to: receive radio resource controlsignaling that comprises the request message.
 20. The apparatus of claim14, wherein the instructions are further executable by the processor tocause the apparatus to: receive the request message at a centralunit-control plane entity included in the base station; forward therequest message from the central unit-control plane entity to a centralunit-machine learning plane entity included in the base station; anddownload, to the central unit-control plane entity, the machine learningmodel, the set of parameters, or the configuration from the machinelearning model repository based at least in part on the request message,wherein transmitting the machine learning model, the set of parameters,or the configuration to the UE is based at least in part on thedownloading.
 21. The apparatus of claim 13, wherein the instructions arefurther executable by the processor to cause the apparatus to: receive,from the UE, an address for the machine learning model, the set ofparameters, or the configuration; and download, for the UE, the machinelearning model, the set of parameters, or the configuration from themachine learning model repository based at least in part on the address.22. The apparatus of claim 13, wherein the instructions are furtherexecutable by the processor to cause the apparatus to: receive, from theUE, an address for a second machine learning model, a second set ofparameters corresponding to the second machine learning model, or asecond configuration corresponding to a second neural network functionof the one or more neural network functions; and upload the secondmachine learning model, the second set of parameters, or the secondconfiguration to the machine learning model repository.
 23. Theapparatus of claim 13, wherein the instructions are further executableby the processor to cause the apparatus to: receive, from the UE at acentral unit-machine learning plane entity included in the base station,an identifier associated with the machine learning model, the set ofparameters, or the configuration; determine an address for the machinelearning model, the set of parameters, or the configuration based atleast in part on the identifier; and download, for the UE, the machinelearning model, the set of parameters, or the configuration from themachine learning model repository based at least in part on the address,wherein transmitting the machine learning model, the set of parameters,or the configuration to the UE is based at least in part on thedownloading.
 24. The apparatus of claim 13, wherein the instructions arefurther executable by the processor to cause the apparatus to: receive,from the UE at a central unit-machine learning plane entity included inthe base station, an identifier associated with a second machinelearning model, a second set of parameters corresponding to the secondmachine learning model, or a second configuration corresponding to asecond neural network function of the one or more neural networkfunctions; determine an address for the second machine learning model,the second set of parameters, or the second configuration based at leastin part on the identifier; and upload, to the machine learning modelrepository, the second machine learning model, the second set ofparameters, or the second configuration based at least in part on theaddress.
 25. A method for wireless communication at a user equipment(UE), comprising: receiving a machine learning model of one or moremachine learning models, a set of parameters corresponding to themachine learning model, or a configuration corresponding to a neuralnetwork function of one or more neural network functions, wherein theone or more machine learning models, the one or more neural networkfunctions, or any combination thereof are associated with a machinelearning model repository that is included in or coupled with a basestation; and receiving, from the base station, an activation message forthe machine learning model, the neural network function, or both. 26.The method of claim 25, further comprising: transmitting, to the basestation, a request message that comprises an indication of the machinelearning model, the neural network function, or both, wherein receivingthe machine learning model, the neural network function, or both isbased at least in part on the request message.
 27. The method of claim26, further comprising: receiving, from the base station, signalingindicating a first set of machine learning models included in ablacklist, a second set of machine learning models included in awhitelist, or both, wherein transmitting the request message is based atleast in part on the machine learning model being included in thewhitelist, excluded from the blacklist, or both.
 28. A method forwireless communication at a base station, comprising: transmitting, to auser equipment (UE), a machine learning model of one or more machinelearning models, a set of parameters corresponding to the machinelearning model, or a configuration corresponding to a neural networkfunction of one or more neural network functions, wherein the one ormore machine learning models, the one or more neural network functions,or any combination thereof are associated with a machine learning modelrepository that is included in or coupled with the base station; andtransmitting, to the UE, an activation message for the machine learningmodel, the neural network function, or both.
 29. The method of claim 28,further comprising: receiving, from the UE, a request message thatincludes an indication of the machine learning model, the neural networkfunction, or both, wherein transmitting the machine learning model, theneural network function, or both is based at least in part on therequest message.
 30. The method of claim 29, further comprising:transmitting, to the UE, signaling indicating a first set of machinelearning models included in a blacklist, a second set of machinelearning models included in a whitelist, or both, wherein the machinelearning model is included in the whitelist, excluded from theblacklist, or both.